Cloud Computing & AWS Internship — 1 Month
You'll learn core Cloud Computing & AWS through structured modules and work on a real live client project alongside senior developers. By the end, you can build confidently and have contributed to something that a real client uses.
40+
live training hours
10
core modules
1–2
live client projects
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for basic 3D orientation. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Codebase Basics
🔒 100+ Sessions • Career-Ready
Core Cloud Computing & AWS modules
Understand cloud computing fundamentals, service models, deployment models, and their use in real-world applications.
Topics covered
Definition, characteristics, and importance of cloud computing
Traditional IT infrastructure versus cloud infrastructure
Cloud deployment models: Public, Private, Hybrid, and Community Cloud
Cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
Benefits of cloud computing: scalability, flexibility, availability, and cost efficiency
Overview of major cloud providers: AWS, Microsoft Azure, and Google Cloud
Basic understanding of cloud pricing and pay-as-you-go usage
What you'll build
- Identify real-world applications of cloud computing and classify them according to the cloud service and deployment models they use.
By the end: Understand cloud computing concepts, service models, deployment models, and the advantages of using cloud infrastructure.
Understand virtualization and how it enables the efficient use of computing resources in cloud environments.
Topics covered
Definition and purpose of virtualization
Physical servers versus virtual machines
Hypervisors and their role in virtualization
Type 1 and Type 2 hypervisors
Virtual CPUs, memory, storage, and network interfaces
Introduction to operating system virtualization and virtual machine management
Benefits and limitations of virtualization
Relationship between virtualization and cloud computing
What you'll build
- Explore a virtual machine using a suitable virtualization platform or demonstration and identify its CPU, memory, storage, and operating system configuration.
By the end: Understand how virtualization creates isolated computing environments and supports the efficient allocation of cloud resources.
Become familiar with AWS, its global infrastructure, and the AWS Management Console.
Topics covered
Introduction to Amazon Web Services and its purpose
Overview of commonly used AWS services
AWS global infrastructure: Regions and Availability Zones
Edge locations and their basic purpose
Introduction to the AWS Management Console
AWS account structure and service navigation
Selecting an AWS Region for resource creation
Basic AWS pricing, Free Tier eligibility, and billing awareness
Shared Responsibility Model: AWS responsibilities versus customer responsibilities
What you'll build
- Access the AWS Management Console, explore the available service categories, select a Region, and identify services related to compute, storage, networking, and security.
By the end: Understand the AWS environment, navigate the Management Console, and identify the purpose of commonly used AWS services.
Understand how AWS manages identities, permissions, and access to cloud resources.
Topics covered
Introduction to AWS Identity and Access Management
IAM users, groups, and roles
IAM policies and permissions
Understanding JSON-based policy structure
Allow and deny permissions
Principle of least privilege
Root user versus IAM identities
Multi-factor authentication (MFA)
Introduction to IAM roles for AWS services
Basic access security and credential protection
What you'll build
- Explore IAM users, roles, and policies in the AWS Console. Examine an example policy and identify which AWS actions it permits or denies.
By the end: Understand how IAM controls access to AWS resources and why permissions should be limited to the access required for a task.
Understand AWS networking fundamentals and how cloud resources communicate within a virtual network.
Topics covered
Introduction to Amazon VPC
VPCs and IP addressing fundamentals
Public and private subnets
IPv4 CIDR blocks and subnet ranges
Route tables and network traffic routing
Internet gateways
NAT gateways and their purpose
Security groups and network access control lists (network ACLs)
Basic inbound and outbound traffic rules
Public versus private resource access
What you'll build
- Explore the default VPC in the AWS Console and identify its subnets, route tables, internet gateway, and security groups.
By the end: Understand how VPCs, subnets, routing, and security controls organize and protect resources within AWS networks.
Understand how to create, configure, access, and manage virtual servers using Amazon EC2.
Topics covered
Introduction to Amazon Elastic Compute Cloud (EC2)
EC2 instance types and use cases
Amazon Machine Images (AMIs)
Instance families and basic sizing considerations
Key pairs and secure instance access
Configuring security groups for EC2
Instance launch configuration
EC2 instance states and lifecycle management
Introduction to Elastic IP addresses
EBS volumes and basic storage configuration
Basic methods of connecting to an instance
Stopping and terminating instances
Basic EC2 pricing considerations
What you'll build
- Launch a suitable EC2 instance in a permitted learning environment, inspect its configuration, understand its security group rules, and practise stopping and terminating it when finished.
By the end: Understand how to launch and manage an EC2 instance and recognize the basic security and cost considerations associated with virtual servers.
Understand object storage and how Amazon S3 is used to store and manage files in the cloud.
Topics covered
Introduction to Amazon S3
Object storage versus block storage
S3 buckets and objects
Bucket naming and Region selection
Uploading, downloading, and deleting objects
Basic S3 storage classes
Bucket policies and access permissions
Block Public Access settings
Versioning and its purpose
Introduction to lifecycle rules
Data protection and basic storage cost considerations
What you'll build
- Create a practice S3 bucket, upload sample files, inspect object properties, and explore versioning and access settings without making sensitive files publicly accessible.
By the end: Understand how to store, retrieve, organize, and protect files using Amazon S3.
Understand managed cloud databases and the basic purpose of Amazon RDS.
Topics covered
Introduction to databases in cloud computing
Relational versus non-relational databases
Overview of Amazon Relational Database Service (RDS)
Supported database engines and common use cases
Database instances, storage, and connectivity
Introduction to database endpoints
Public versus private database access
Security groups and database connectivity
Automated backups and snapshots
Basic database availability and maintenance concepts
Overview of Amazon DynamoDB as a managed NoSQL database
Database pricing and resource management
What you'll build
- Explore the Amazon RDS Console and identify database engine, instance configuration, connectivity, backup, and security settings. Review how an application would connect to a database endpoint.
By the end: Understand managed database services in AWS and the basic configuration, connectivity, security, and backup considerations for cloud databases.
Understand how to monitor AWS resources, identify operational issues, and manage cloud expenditure.
Topics covered
Introduction to Amazon CloudWatch
CloudWatch metrics, logs, and alarms
Monitoring EC2 resource metrics
Basic resource health and availability monitoring
Introduction to AWS CloudTrail and API activity auditing
Overview of AWS billing and cost management
Understanding usage-based pricing
AWS Budgets and budget alerts
Identifying unused or unnecessary resources
Basic cost optimization practices
Importance of stopping or deleting resources that are no longer needed
What you'll build
- Explore CloudWatch metrics for an available resource, examine example logs or alarms, and review the AWS billing and budget interfaces available to the account.
By the end: Understand how AWS resources are monitored, how account activity can be audited, and how cloud costs can be tracked and controlled.
Understand serverless architecture and how AWS services can execute application logic without requiring users to manage servers directly.
Topics covered
Introduction to serverless computing
Traditional server-based versus serverless architecture
AWS Lambda and its purpose
Lambda functions, handlers, and execution basics
Events and event-driven execution
Lambda triggers and supported integrations
Introduction to Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Basic execution logs in CloudWatch
Error handling and function timeouts
Basic serverless pricing concepts
What you'll build
- Create or inspect a simple Lambda function and understand how an API Gateway route can invoke it. Review the function's execution logs and observe the response returned by the API.
By the end: Understand the fundamentals of serverless applications and how AWS Lambda, API Gateway, and CloudWatch work together.
Live client projects
In the 1-month program you work on one live client project alongside your mentor. These are real projects built for actual client requirements — not practice exercises. You build on the same codebase your seniors are working on.
Live client project
Hash-Based Auto-Deploy System
Git commit hash tracking, automated container build triggers, artifact validation, and immutable multi-environment deployment.
AWS EC2S3DockerGitHub ActionsBash
Live client project
Cloud Deployment Approval & Rollback Platform (CDARP)
Multi-stage release approval gates, automated smoke testing, instant one-click canary rollback, and deployment audit logging.
AWS LambdaAPI GatewayDynamoDBCloudWatchPython
Everything included in your 1-month enrollment
✓
40+ live training hours with senior developers✓
Limited VR orientation sessions (introduction to 3D codebase visualizer)✓
10 core Cloud Computing & AWS modules with practical exercises✓
1 live client project on an active production codebase✓
Internship completion certificate from WhiteandBox.com✓
Remote, hybrid, or in-office at Bangalore1-Month Cloud Computing & AWS Internship
Live sessions · Real project · Certificate and offer letter
₹5,000
₹2,500
50% off
1 month · live daily sessions
Want to go deeper into every topic?
The 2–6 month program covers the same 10 modules in significantly more depth — more exercises, more concepts, and 5 additional advanced modules that the 1-month doesn't reach.
Cloud Computing & AWS Internship — 2 to 6 Months
The same core Cloud Computing & AWS curriculum, covered in significantly more depth — more exercises, more edge cases, more patterns. Plus five additional modules that take you into advanced concepts, system architectures, and production delivery. With more time comes stronger ability, a deeper portfolio, and real exposure to how a professional team ships software.
120+
live training hours
15+
modules
2–3
live client projects
Everything in the 1-month program is included here. The same 10 core modules — but every one of them goes deeper: more exercises, more business scenarios, more edge cases. On top of that, you get five additional modules that the 1-month doesn't have time to cover.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for project development lab. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Architectural Walkthroughs
🔒 100+ Sessions • Career-Ready
Foundation modules — all 10, covered in full depth
Understand cloud computing fundamentals, service models, deployment models, and their use in real-world applications.
Topics covered
Definition, characteristics, and importance of cloud computing
Traditional IT infrastructure versus cloud infrastructure
Cloud deployment models: Public, Private, Hybrid, and Community Cloud
Cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
Benefits of cloud computing: scalability, flexibility, availability, and cost efficiency
Overview of major cloud providers: AWS, Microsoft Azure, and Google Cloud
Basic understanding of cloud pricing and pay-as-you-go usage
What you'll build
- Identify real-world applications of cloud computing and classify them according to the cloud service and deployment models they use.
By the end: Understand cloud computing concepts, service models, deployment models, and the advantages of using cloud infrastructure.
Understand virtualization and how it enables the efficient use of computing resources in cloud environments.
Topics covered
Definition and purpose of virtualization
Physical servers versus virtual machines
Hypervisors and their role in virtualization
Type 1 and Type 2 hypervisors
Virtual CPUs, memory, storage, and network interfaces
Introduction to operating system virtualization and virtual machine management
Benefits and limitations of virtualization
Relationship between virtualization and cloud computing
What you'll build
- Explore a virtual machine using a suitable virtualization platform or demonstration and identify its CPU, memory, storage, and operating system configuration.
By the end: Understand how virtualization creates isolated computing environments and supports the efficient allocation of cloud resources.
Become familiar with AWS, its global infrastructure, and the AWS Management Console.
Topics covered
Introduction to Amazon Web Services and its purpose
Overview of commonly used AWS services
AWS global infrastructure: Regions and Availability Zones
Edge locations and their basic purpose
Introduction to the AWS Management Console
AWS account structure and service navigation
Selecting an AWS Region for resource creation
Basic AWS pricing, Free Tier eligibility, and billing awareness
Shared Responsibility Model: AWS responsibilities versus customer responsibilities
What you'll build
- Access the AWS Management Console, explore the available service categories, select a Region, and identify services related to compute, storage, networking, and security.
By the end: Understand the AWS environment, navigate the Management Console, and identify the purpose of commonly used AWS services.
Understand how AWS manages identities, permissions, and access to cloud resources.
Topics covered
Introduction to AWS Identity and Access Management
IAM users, groups, and roles
IAM policies and permissions
Understanding JSON-based policy structure
Allow and deny permissions
Principle of least privilege
Root user versus IAM identities
Multi-factor authentication (MFA)
Introduction to IAM roles for AWS services
Basic access security and credential protection
What you'll build
- Explore IAM users, roles, and policies in the AWS Console. Examine an example policy and identify which AWS actions it permits or denies.
By the end: Understand how IAM controls access to AWS resources and why permissions should be limited to the access required for a task.
Understand AWS networking fundamentals and how cloud resources communicate within a virtual network.
Topics covered
Introduction to Amazon VPC
VPCs and IP addressing fundamentals
Public and private subnets
IPv4 CIDR blocks and subnet ranges
Route tables and network traffic routing
Internet gateways
NAT gateways and their purpose
Security groups and network access control lists (network ACLs)
Basic inbound and outbound traffic rules
Public versus private resource access
What you'll build
- Explore the default VPC in the AWS Console and identify its subnets, route tables, internet gateway, and security groups.
By the end: Understand how VPCs, subnets, routing, and security controls organize and protect resources within AWS networks.
Understand how to create, configure, access, and manage virtual servers using Amazon EC2.
Topics covered
Introduction to Amazon Elastic Compute Cloud (EC2)
EC2 instance types and use cases
Amazon Machine Images (AMIs)
Instance families and basic sizing considerations
Key pairs and secure instance access
Configuring security groups for EC2
Instance launch configuration
EC2 instance states and lifecycle management
Introduction to Elastic IP addresses
EBS volumes and basic storage configuration
Basic methods of connecting to an instance
Stopping and terminating instances
Basic EC2 pricing considerations
What you'll build
- Launch a suitable EC2 instance in a permitted learning environment, inspect its configuration, understand its security group rules, and practise stopping and terminating it when finished.
By the end: Understand how to launch and manage an EC2 instance and recognize the basic security and cost considerations associated with virtual servers.
Understand object storage and how Amazon S3 is used to store and manage files in the cloud.
Topics covered
Introduction to Amazon S3
Object storage versus block storage
S3 buckets and objects
Bucket naming and Region selection
Uploading, downloading, and deleting objects
Basic S3 storage classes
Bucket policies and access permissions
Block Public Access settings
Versioning and its purpose
Introduction to lifecycle rules
Data protection and basic storage cost considerations
What you'll build
- Create a practice S3 bucket, upload sample files, inspect object properties, and explore versioning and access settings without making sensitive files publicly accessible.
By the end: Understand how to store, retrieve, organize, and protect files using Amazon S3.
Understand managed cloud databases and the basic purpose of Amazon RDS.
Topics covered
Introduction to databases in cloud computing
Relational versus non-relational databases
Overview of Amazon Relational Database Service (RDS)
Supported database engines and common use cases
Database instances, storage, and connectivity
Introduction to database endpoints
Public versus private database access
Security groups and database connectivity
Automated backups and snapshots
Basic database availability and maintenance concepts
Overview of Amazon DynamoDB as a managed NoSQL database
Database pricing and resource management
What you'll build
- Explore the Amazon RDS Console and identify database engine, instance configuration, connectivity, backup, and security settings. Review how an application would connect to a database endpoint.
By the end: Understand managed database services in AWS and the basic configuration, connectivity, security, and backup considerations for cloud databases.
Understand how to monitor AWS resources, identify operational issues, and manage cloud expenditure.
Topics covered
Introduction to Amazon CloudWatch
CloudWatch metrics, logs, and alarms
Monitoring EC2 resource metrics
Basic resource health and availability monitoring
Introduction to AWS CloudTrail and API activity auditing
Overview of AWS billing and cost management
Understanding usage-based pricing
AWS Budgets and budget alerts
Identifying unused or unnecessary resources
Basic cost optimization practices
Importance of stopping or deleting resources that are no longer needed
What you'll build
- Explore CloudWatch metrics for an available resource, examine example logs or alarms, and review the AWS billing and budget interfaces available to the account.
By the end: Understand how AWS resources are monitored, how account activity can be audited, and how cloud costs can be tracked and controlled.
Understand serverless architecture and how AWS services can execute application logic without requiring users to manage servers directly.
Topics covered
Introduction to serverless computing
Traditional server-based versus serverless architecture
AWS Lambda and its purpose
Lambda functions, handlers, and execution basics
Events and event-driven execution
Lambda triggers and supported integrations
Introduction to Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Basic execution logs in CloudWatch
Error handling and function timeouts
Basic serverless pricing concepts
What you'll build
- Create or inspect a simple Lambda function and understand how an API Gateway route can invoke it. Review the function's execution logs and observe the response returned by the API.
By the end: Understand the fundamentals of serverless applications and how AWS Lambda, API Gateway, and CloudWatch work together.
Additional modules — topics the 1-month doesn't reach
Understand how domain names are resolved and how application content is delivered efficiently.
Topics covered
DNS fundamentals
Domain names and DNS records
A, AAAA, CNAME, and alias records
Amazon Route 53 hosted zones
Routing policies overview
Health checks and DNS failover fundamentals
Content delivery networks (CDNs)
Amazon CloudFront distributions
Origins, cache behaviours, and cache invalidation
HTTPS and TLS certificate fundamentals
Amazon Certificate Manager overview
Static website delivery
Basic performance and security considerations
What you'll build
- Draw the request flow for a website using Route 53, CloudFront, and an S3 or application origin.
By the end: Learners understand DNS resolution, content caching, and how Route 53 and CloudFront support application delivery.
Understand how AWS resources and application activity are monitored and investigated.
Topics covered
Introduction to Amazon CloudWatch
Metrics, logs, and alarms
EC2 monitoring
CloudWatch dashboards
Log groups and log streams
Basic application log analysis
CloudWatch alarms and notifications
Amazon CloudTrail
Management events and API activity auditing
AWS Config fundamentals
Resource configuration history
Monitoring availability and performance
Basic alert investigation
Operational logging best practices
What you'll build
- Examine CloudWatch metrics and logs for an available resource, identify a suitable alarm condition, and explain how CloudTrail can help investigate resource changes.
By the end: Learners understand the fundamentals of cloud monitoring, log analysis, and auditing of AWS account activity.
Understand the major security controls used to protect AWS infrastructure, applications, and data.
Topics covered
AWS Shared Responsibility Model
Identity and access management practices
Network security and resource isolation
Security groups and network ACLs
Encryption at rest and in transit
AWS Key Management Service (KMS) fundamentals
Secrets Manager and Systems Manager Parameter Store
Secure storage permissions
Logging and auditing
AWS WAF overview
AWS Shield overview
Vulnerability management fundamentals
Data protection and secure deletion
Security incident response fundamentals
What you'll build
- Review a sample cloud architecture, identify common security weaknesses, and recommend improvements to identity permissions, network access, encryption, and logging.
By the end: Learners can identify common AWS security risks and explain appropriate controls for protecting cloud resources.
Understand how to protect workloads and plan recovery from infrastructure or data failures.
Topics covered
Backup and recovery fundamentals
Backup policies and schedules
Amazon EBS snapshots
Amazon S3 versioning
AWS Backup overview
Database backups and snapshots
Cross-Region and cross-account backup concepts
Recovery Point Objective (RPO)
Recovery Time Objective (RTO)
Disaster recovery strategies
High availability versus disaster recovery
Restore testing and recovery validation
Data retention and backup costs
What you'll build
- Prepare a backup and recovery plan for a small application containing an EC2 instance, an S3 bucket, and a database.
By the end: Learners understand how backups, recovery targets, and disaster recovery planning help reduce the impact of service failures and data loss.
Understand how AWS supports application development without requiring direct management of underlying servers.
Topics covered
Server-based versus serverless architecture
Serverless computing principles
AWS Lambda
Lambda functions, handlers, and runtime environments
Event sources and triggers
Execution roles and permissions
Function configuration, timeouts, and memory
Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Amazon EventBridge fundamentals
Amazon SQS and SNS overview
CloudWatch logs for Lambda
Serverless error handling and cost considerations
What you'll build
- Design a simple serverless application in which an API Gateway route invokes a Lambda function and returns a response.
By the end: Learners understand the components of a basic serverless architecture and how event-driven services communicate.
Live client projects
In the extended program you work on 2–3 live client projects across the duration. You're not just doing exercises — you're contributing to software a real client is waiting to receive.
Live client project
Hash-Based Auto-Deploy System
Git commit hash tracking, automated container build triggers, artifact validation, and immutable multi-environment deployment.
AWS EC2S3DockerGitHub ActionsBash
Live client project
Cloud Deployment Approval & Rollback Platform (CDARP)
Multi-stage release approval gates, automated smoke testing, instant one-click canary rollback, and deployment audit logging.
AWS LambdaAPI GatewayDynamoDBCloudWatchPython
Live client project
Internal Developer Platform (IDP)
Self-service cloud environment provisioning, developer access governance, resource templates, and infrastructure cost metrics.
AWS VPCTerraformECSPythonDocker
Live client project
Cloud Sandbox Management Platform
Ephemeral test environment provisioning, automatic idle-resource destruction, budget capping, and security policy enforcement.
AWS CloudFormationLambdaIAMEventBridgePython
Everything included in your 2–6 month enrollment
✓
120+ live training hours with senior developers✓
Limited VR project sessions (interactive debugging & architecture walkthroughs)✓
15+ modules: all 10 core + 5 advanced modules✓
2–3 live client projects with production code review✓
Internship completion certificate + Letter of Recommendation✓
LinkedIn profile optimization & resume review✓
Remote, hybrid, or in-office at Bangalore2–6 Month Cloud Computing & AWS Internship
Deeper learning · More projects · Stronger portfolio
₹9,000
₹4,500
50% off
2 / 3 / 4 / 6 months · live daily sessions
Ready for the complete career-ready path?
The Career-Ready flagship bundles everything here with the full Cloud Computing & AWS career course, industry project standards, and 25 AI-powered development modules — 400+ live hours total. The program serious developers choose when they want job outcomes, not just a certificate.
Career-Ready Program — Flagship
The complete path from zero to job-ready. Four programs bundled into one: the full Cloud Computing & AWS internship, the advanced Cloud Computing & AWS career course, industry-grade project engineering, and 25+ modules on AI-powered development. Every day is five hours of live training. This is the program for students who want a real outcome — not just a certificate on a shelf.
400+
live training hours
4
full programs
5+
live client projects
How the 400+ hours break down: 5 live hours per day, 30 hours per week, approximately 120 hours per month — plus extra mentorship, modular support sessions, project reviews, and code walkthroughs on top of scheduled training. Over a 3–6 month program, this compounds to 400+ contact hours.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
100+ VR SessionsUnlimited Revision (2 Yrs)
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
Program 1 — Cloud Computing & AWS Internship (all 15 modules, complete in full depth)
Complete Foundational Track Included
In the Flagship program, you complete all 15 modules of the internship in full depth, building enterprise capabilities before advancing to specialized architecture.
Understand cloud computing fundamentals, service models, deployment models, and their use in real-world applications.
Topics covered
Definition, characteristics, and importance of cloud computing
Traditional IT infrastructure versus cloud infrastructure
Cloud deployment models: Public, Private, Hybrid, and Community Cloud
Cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
Benefits of cloud computing: scalability, flexibility, availability, and cost efficiency
Overview of major cloud providers: AWS, Microsoft Azure, and Google Cloud
Basic understanding of cloud pricing and pay-as-you-go usage
What you'll build
- Identify real-world applications of cloud computing and classify them according to the cloud service and deployment models they use.
By the end: Understand cloud computing concepts, service models, deployment models, and the advantages of using cloud infrastructure.
Understand virtualization and how it enables the efficient use of computing resources in cloud environments.
Topics covered
Definition and purpose of virtualization
Physical servers versus virtual machines
Hypervisors and their role in virtualization
Type 1 and Type 2 hypervisors
Virtual CPUs, memory, storage, and network interfaces
Introduction to operating system virtualization and virtual machine management
Benefits and limitations of virtualization
Relationship between virtualization and cloud computing
What you'll build
- Explore a virtual machine using a suitable virtualization platform or demonstration and identify its CPU, memory, storage, and operating system configuration.
By the end: Understand how virtualization creates isolated computing environments and supports the efficient allocation of cloud resources.
Become familiar with AWS, its global infrastructure, and the AWS Management Console.
Topics covered
Introduction to Amazon Web Services and its purpose
Overview of commonly used AWS services
AWS global infrastructure: Regions and Availability Zones
Edge locations and their basic purpose
Introduction to the AWS Management Console
AWS account structure and service navigation
Selecting an AWS Region for resource creation
Basic AWS pricing, Free Tier eligibility, and billing awareness
Shared Responsibility Model: AWS responsibilities versus customer responsibilities
What you'll build
- Access the AWS Management Console, explore the available service categories, select a Region, and identify services related to compute, storage, networking, and security.
By the end: Understand the AWS environment, navigate the Management Console, and identify the purpose of commonly used AWS services.
Understand how AWS manages identities, permissions, and access to cloud resources.
Topics covered
Introduction to AWS Identity and Access Management
IAM users, groups, and roles
IAM policies and permissions
Understanding JSON-based policy structure
Allow and deny permissions
Principle of least privilege
Root user versus IAM identities
Multi-factor authentication (MFA)
Introduction to IAM roles for AWS services
Basic access security and credential protection
What you'll build
- Explore IAM users, roles, and policies in the AWS Console. Examine an example policy and identify which AWS actions it permits or denies.
By the end: Understand how IAM controls access to AWS resources and why permissions should be limited to the access required for a task.
Understand AWS networking fundamentals and how cloud resources communicate within a virtual network.
Topics covered
Introduction to Amazon VPC
VPCs and IP addressing fundamentals
Public and private subnets
IPv4 CIDR blocks and subnet ranges
Route tables and network traffic routing
Internet gateways
NAT gateways and their purpose
Security groups and network access control lists (network ACLs)
Basic inbound and outbound traffic rules
Public versus private resource access
What you'll build
- Explore the default VPC in the AWS Console and identify its subnets, route tables, internet gateway, and security groups.
By the end: Understand how VPCs, subnets, routing, and security controls organize and protect resources within AWS networks.
Understand how to create, configure, access, and manage virtual servers using Amazon EC2.
Topics covered
Introduction to Amazon Elastic Compute Cloud (EC2)
EC2 instance types and use cases
Amazon Machine Images (AMIs)
Instance families and basic sizing considerations
Key pairs and secure instance access
Configuring security groups for EC2
Instance launch configuration
EC2 instance states and lifecycle management
Introduction to Elastic IP addresses
EBS volumes and basic storage configuration
Basic methods of connecting to an instance
Stopping and terminating instances
Basic EC2 pricing considerations
What you'll build
- Launch a suitable EC2 instance in a permitted learning environment, inspect its configuration, understand its security group rules, and practise stopping and terminating it when finished.
By the end: Understand how to launch and manage an EC2 instance and recognize the basic security and cost considerations associated with virtual servers.
Understand object storage and how Amazon S3 is used to store and manage files in the cloud.
Topics covered
Introduction to Amazon S3
Object storage versus block storage
S3 buckets and objects
Bucket naming and Region selection
Uploading, downloading, and deleting objects
Basic S3 storage classes
Bucket policies and access permissions
Block Public Access settings
Versioning and its purpose
Introduction to lifecycle rules
Data protection and basic storage cost considerations
What you'll build
- Create a practice S3 bucket, upload sample files, inspect object properties, and explore versioning and access settings without making sensitive files publicly accessible.
By the end: Understand how to store, retrieve, organize, and protect files using Amazon S3.
Understand managed cloud databases and the basic purpose of Amazon RDS.
Topics covered
Introduction to databases in cloud computing
Relational versus non-relational databases
Overview of Amazon Relational Database Service (RDS)
Supported database engines and common use cases
Database instances, storage, and connectivity
Introduction to database endpoints
Public versus private database access
Security groups and database connectivity
Automated backups and snapshots
Basic database availability and maintenance concepts
Overview of Amazon DynamoDB as a managed NoSQL database
Database pricing and resource management
What you'll build
- Explore the Amazon RDS Console and identify database engine, instance configuration, connectivity, backup, and security settings. Review how an application would connect to a database endpoint.
By the end: Understand managed database services in AWS and the basic configuration, connectivity, security, and backup considerations for cloud databases.
Understand how to monitor AWS resources, identify operational issues, and manage cloud expenditure.
Topics covered
Introduction to Amazon CloudWatch
CloudWatch metrics, logs, and alarms
Monitoring EC2 resource metrics
Basic resource health and availability monitoring
Introduction to AWS CloudTrail and API activity auditing
Overview of AWS billing and cost management
Understanding usage-based pricing
AWS Budgets and budget alerts
Identifying unused or unnecessary resources
Basic cost optimization practices
Importance of stopping or deleting resources that are no longer needed
What you'll build
- Explore CloudWatch metrics for an available resource, examine example logs or alarms, and review the AWS billing and budget interfaces available to the account.
By the end: Understand how AWS resources are monitored, how account activity can be audited, and how cloud costs can be tracked and controlled.
Understand serverless architecture and how AWS services can execute application logic without requiring users to manage servers directly.
Topics covered
Introduction to serverless computing
Traditional server-based versus serverless architecture
AWS Lambda and its purpose
Lambda functions, handlers, and execution basics
Events and event-driven execution
Lambda triggers and supported integrations
Introduction to Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Basic execution logs in CloudWatch
Error handling and function timeouts
Basic serverless pricing concepts
What you'll build
- Create or inspect a simple Lambda function and understand how an API Gateway route can invoke it. Review the function's execution logs and observe the response returned by the API.
By the end: Understand the fundamentals of serverless applications and how AWS Lambda, API Gateway, and CloudWatch work together.
Understand how domain names are resolved and how application content is delivered efficiently.
Topics covered
DNS fundamentals
Domain names and DNS records
A, AAAA, CNAME, and alias records
Amazon Route 53 hosted zones
Routing policies overview
Health checks and DNS failover fundamentals
Content delivery networks (CDNs)
Amazon CloudFront distributions
Origins, cache behaviours, and cache invalidation
HTTPS and TLS certificate fundamentals
Amazon Certificate Manager overview
Static website delivery
Basic performance and security considerations
What you'll build
- Draw the request flow for a website using Route 53, CloudFront, and an S3 or application origin.
By the end: Learners understand DNS resolution, content caching, and how Route 53 and CloudFront support application delivery.
Understand how AWS resources and application activity are monitored and investigated.
Topics covered
Introduction to Amazon CloudWatch
Metrics, logs, and alarms
EC2 monitoring
CloudWatch dashboards
Log groups and log streams
Basic application log analysis
CloudWatch alarms and notifications
Amazon CloudTrail
Management events and API activity auditing
AWS Config fundamentals
Resource configuration history
Monitoring availability and performance
Basic alert investigation
Operational logging best practices
What you'll build
- Examine CloudWatch metrics and logs for an available resource, identify a suitable alarm condition, and explain how CloudTrail can help investigate resource changes.
By the end: Learners understand the fundamentals of cloud monitoring, log analysis, and auditing of AWS account activity.
Understand the major security controls used to protect AWS infrastructure, applications, and data.
Topics covered
AWS Shared Responsibility Model
Identity and access management practices
Network security and resource isolation
Security groups and network ACLs
Encryption at rest and in transit
AWS Key Management Service (KMS) fundamentals
Secrets Manager and Systems Manager Parameter Store
Secure storage permissions
Logging and auditing
AWS WAF overview
AWS Shield overview
Vulnerability management fundamentals
Data protection and secure deletion
Security incident response fundamentals
What you'll build
- Review a sample cloud architecture, identify common security weaknesses, and recommend improvements to identity permissions, network access, encryption, and logging.
By the end: Learners can identify common AWS security risks and explain appropriate controls for protecting cloud resources.
Understand how to protect workloads and plan recovery from infrastructure or data failures.
Topics covered
Backup and recovery fundamentals
Backup policies and schedules
Amazon EBS snapshots
Amazon S3 versioning
AWS Backup overview
Database backups and snapshots
Cross-Region and cross-account backup concepts
Recovery Point Objective (RPO)
Recovery Time Objective (RTO)
Disaster recovery strategies
High availability versus disaster recovery
Restore testing and recovery validation
Data retention and backup costs
What you'll build
- Prepare a backup and recovery plan for a small application containing an EC2 instance, an S3 bucket, and a database.
By the end: Learners understand how backups, recovery targets, and disaster recovery planning help reduce the impact of service failures and data loss.
Understand how AWS supports application development without requiring direct management of underlying servers.
Topics covered
Server-based versus serverless architecture
Serverless computing principles
AWS Lambda
Lambda functions, handlers, and runtime environments
Event sources and triggers
Execution roles and permissions
Function configuration, timeouts, and memory
Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Amazon EventBridge fundamentals
Amazon SQS and SNS overview
CloudWatch logs for Lambda
Serverless error handling and cost considerations
What you'll build
- Design a simple serverless application in which an API Gateway route invokes a Lambda function and returns a response.
By the end: Learners understand the components of a basic serverless architecture and how event-driven services communicate.
Program 2 — Full Cloud Computing & AWS Career Course
Understand the fundamentals of cloud computing, its evolution, service models, and applications in the IT industry.
Topics covered
Definition, characteristics, and evolution of cloud computing
Traditional IT infrastructure versus cloud infrastructure
Benefits and limitations of cloud computing
Cloud deployment models:
Public cloud
Private cloud
Hybrid cloud
Community cloud
Cloud service models:
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Introduction to cloud providers: AWS, Microsoft Azure, and Google Cloud
Scalability, elasticity, availability, and reliability
Pay-as-you-go pricing and resource consumption
Common industry use cases for cloud computing
What you'll build
- Identify suitable cloud service and deployment models for a startup application, an enterprise database, and an online shopping platform.
By the end: Learners understand core cloud computing concepts and can distinguish between cloud service models, deployment models, and business use cases.
Understand virtualization and the infrastructure concepts that enable cloud computing.
Topics covered
Introduction to virtualization
Physical servers versus virtual machines
Hypervisors and their purpose
Type 1 and Type 2 hypervisors
Virtual CPUs, memory, storage, and network interfaces
Virtual machine provisioning and resource allocation
Virtualization isolation and resource sharing
Introduction to containers and how they differ from virtual machines
Virtualization in data centres
Relationship between virtualization and cloud computing
Introduction to software-defined infrastructure
Benefits, limitations, and common virtualization use cases
What you'll build
- Create or inspect a virtual machine using an available virtualization platform and identify its CPU, RAM, storage, network configuration, and operating system.
By the end: Learners understand how virtualization enables resource sharing and provides the foundation for cloud infrastructure.
Understand AWS architecture, service categories, account structure, and global infrastructure.
Topics covered
Introduction to Amazon Web Services
Overview of commonly used AWS services
AWS global infrastructure:
Regions
Availability Zones
Edge locations
AWS Management Console
AWS account structure and service navigation
Selecting Regions for resource deployment
Availability, fault tolerance, and disaster recovery fundamentals
AWS Shared Responsibility Model
AWS documentation and service reference resources
Introduction to AWS pricing and billing
AWS Free Tier eligibility and usage considerations
AWS account security and root-user protection
What you'll build
- Explore the AWS Management Console, identify major service categories, compare available Regions, and locate the configuration pages for compute, storage, networking, and security.
By the end: Learners can navigate the AWS Console and explain how AWS global infrastructure supports application deployment and availability.
Understand how AWS identities, permissions, and access policies secure cloud resources.
Topics covered
Introduction to AWS Identity and Access Management
IAM users, groups, and roles
IAM policies and permission evaluation
JSON structure of IAM policies
Allow and deny permissions
Identity-based and resource-based policies
Principle of least privilege
Root user versus IAM identities
Multi-factor authentication (MFA)
IAM roles for AWS services
Temporary credentials and role assumption
Access keys and credential protection
IAM policy troubleshooting fundamentals
Basic identity and access security practices
What you'll build
- Examine example IAM policies, identify allowed and denied actions, and configure a limited-permission identity or role in an authorized practice account.
By the end: Learners understand AWS access control and can explain how IAM policies and roles protect cloud resources.
Build the networking knowledge required to design and troubleshoot cloud environments.
Topics covered
Networking fundamentals and communication models
OSI and TCP/IP models
IPv4 and IPv6 fundamentals
Public and private IP addresses
Subnet masks and CIDR notation
Network ports and common protocols
DNS fundamentals
HTTP and HTTPS
Routing and packet flow
TCP versus UDP
Firewalls and network access controls
Introduction to network troubleshooting
Public internet communication and private networking
What you'll build
- Calculate basic IPv4 subnet ranges, identify common application ports, and trace the expected network path between a client and a cloud-hosted application.
By the end: Learners understand IP addressing, subnetting, routing, and common network protocols used in cloud environments.
Understand how AWS networking resources are configured to create isolated and controlled cloud networks.
Topics covered
Introduction to Amazon VPC
VPC creation and IP address ranges
IPv4 CIDR blocks and subnet planning
Public and private subnets
Route tables and route propagation fundamentals
Internet gateways
NAT gateways and outbound internet access
Security groups
Network access control lists (network ACLs)
Security groups versus network ACLs
VPC peering fundamentals
VPC endpoints and private service access
VPC Flow Logs
Basic VPC troubleshooting
Network segmentation and security considerations
What you'll build
- Design a VPC containing public and private subnets, document its routing requirements, and explore how security groups control access to resources.
By the end: Learners understand VPC architecture and can explain how subnets, routing, gateways, and security controls work together.
Learn how to provision, configure, access, and manage virtual servers in AWS.
Topics covered
Introduction to Amazon Elastic Compute Cloud (EC2)
EC2 instance types and families
Choosing instance types based on workload
Amazon Machine Images (AMIs)
EC2 launch configurations
Key pairs and secure instance access
Security groups and instance connectivity
Instance lifecycle: pending, running, stopping, stopped, and terminated
Elastic IP addresses
Elastic Block Store (EBS) volumes
Instance metadata and user data
Basic Linux and Windows instance administration
Connecting through SSH, Remote Desktop, or supported AWS access methods
EC2 pricing and purchasing options
Instance monitoring and troubleshooting
What you'll build
- Launch an appropriately sized EC2 instance, configure restricted access, connect using an approved method, inspect system resources, and practise stopping and terminating the instance.
By the end: Learners understand EC2 provisioning, configuration, connectivity, storage attachment, and basic server administration.
Understand AWS storage services and select appropriate storage based on application requirements.
Topics covered
Introduction to cloud storage
Block, object, and file storage
Amazon Elastic Block Store (EBS)
EBS volume types and snapshots
Amazon Simple Storage Service (S3)
S3 buckets, objects, and storage classes
S3 versioning and lifecycle management
Bucket policies and access controls
S3 Block Public Access
Amazon Elastic File System (EFS)
Introduction to Amazon FSx
Storage encryption
Backup and recovery fundamentals
Storage performance, durability, and cost considerations
What you'll build
- Create an S3 bucket, upload sample files, inspect access controls, and compare when EBS, S3, and EFS would be appropriate for different application requirements.
By the end: Learners understand the differences between AWS storage services and can select suitable storage for common workloads.
Understand managed database services and their use in cloud-based applications.
Topics covered
Database fundamentals and cloud database concepts
Relational versus non-relational databases
Amazon Relational Database Service (RDS)
Supported RDS database engines
Database instances, storage, and endpoints
Database connectivity and security groups
Automated backups and snapshots
Multi-AZ deployments
Read replicas
Amazon Aurora overview
Amazon DynamoDB fundamentals
Tables, items, keys, and indexes in DynamoDB
Database encryption and access control
Database monitoring and performance basics
Database selection based on application requirements
What you'll build
- Explore the RDS and DynamoDB consoles and prepare a comparison of database choices for an inventory application and a high-volume key-value workload.
By the end: Learners understand managed relational and NoSQL database services and their main configuration and security considerations.
Understand how AWS distributes application traffic and adjusts computing capacity based on demand.
Topics covered
Introduction to load balancing
Benefits of distributing application traffic
Elastic Load Balancing overview
Application Load Balancer (ALB)
Network Load Balancer (NLB)
Target groups and health checks
Listeners and listener rules
Introduction to EC2 Auto Scaling
Launch templates
Minimum, desired, and maximum capacity
Scaling policies
Target tracking and scheduled scaling
High availability across Availability Zones
Basic load balancer troubleshooting
Cost and capacity considerations
What you'll build
- Design a web application architecture that uses a load balancer and an Auto Scaling group to support changing traffic levels.
By the end: Learners understand how load balancing and Auto Scaling improve availability and help applications respond to changing demand.
Understand how domain names are resolved and how application content is delivered efficiently.
Topics covered
DNS fundamentals
Domain names and DNS records
A, AAAA, CNAME, and alias records
Amazon Route 53 hosted zones
Routing policies overview
Health checks and DNS failover fundamentals
Content delivery networks (CDNs)
Amazon CloudFront distributions
Origins, cache behaviours, and cache invalidation
HTTPS and TLS certificate fundamentals
Amazon Certificate Manager overview
Static website delivery
Basic performance and security considerations
What you'll build
- Draw the request flow for a website using Route 53, CloudFront, and an S3 or application origin.
By the end: Learners understand DNS resolution, content caching, and how Route 53 and CloudFront support application delivery.
Understand how AWS resources and application activity are monitored and investigated.
Topics covered
Introduction to Amazon CloudWatch
Metrics, logs, and alarms
EC2 monitoring
CloudWatch dashboards
Log groups and log streams
Basic application log analysis
CloudWatch alarms and notifications
Amazon CloudTrail
Management events and API activity auditing
AWS Config fundamentals
Resource configuration history
Monitoring availability and performance
Basic alert investigation
Operational logging best practices
What you'll build
- Examine CloudWatch metrics and logs for an available resource, identify a suitable alarm condition, and explain how CloudTrail can help investigate resource changes.
By the end: Learners understand the fundamentals of cloud monitoring, log analysis, and auditing of AWS account activity.
Understand the major security controls used to protect AWS infrastructure, applications, and data.
Topics covered
AWS Shared Responsibility Model
Identity and access management practices
Network security and resource isolation
Security groups and network ACLs
Encryption at rest and in transit
AWS Key Management Service (KMS) fundamentals
Secrets Manager and Systems Manager Parameter Store
Secure storage permissions
Logging and auditing
AWS WAF overview
AWS Shield overview
Vulnerability management fundamentals
Data protection and secure deletion
Security incident response fundamentals
What you'll build
- Review a sample cloud architecture, identify common security weaknesses, and recommend improvements to identity permissions, network access, encryption, and logging.
By the end: Learners can identify common AWS security risks and explain appropriate controls for protecting cloud resources.
Understand how to protect workloads and plan recovery from infrastructure or data failures.
Topics covered
Backup and recovery fundamentals
Backup policies and schedules
Amazon EBS snapshots
Amazon S3 versioning
AWS Backup overview
Database backups and snapshots
Cross-Region and cross-account backup concepts
Recovery Point Objective (RPO)
Recovery Time Objective (RTO)
Disaster recovery strategies
High availability versus disaster recovery
Restore testing and recovery validation
Data retention and backup costs
What you'll build
- Prepare a backup and recovery plan for a small application containing an EC2 instance, an S3 bucket, and a database.
By the end: Learners understand how backups, recovery targets, and disaster recovery planning help reduce the impact of service failures and data loss.
Understand how AWS supports application development without requiring direct management of underlying servers.
Topics covered
Server-based versus serverless architecture
Serverless computing principles
AWS Lambda
Lambda functions, handlers, and runtime environments
Event sources and triggers
Execution roles and permissions
Function configuration, timeouts, and memory
Amazon API Gateway
API endpoints and HTTP methods
Integrating API Gateway with Lambda
Amazon EventBridge fundamentals
Amazon SQS and SNS overview
CloudWatch logs for Lambda
Serverless error handling and cost considerations
What you'll build
- Design a simple serverless application in which an API Gateway route invokes a Lambda function and returns a response.
By the end: Learners understand the components of a basic serverless architecture and how event-driven services communicate.
Understand how independent application components communicate reliably using AWS integration services.
Topics covered
Introduction to loosely coupled architecture
Synchronous versus asynchronous communication
Amazon Simple Queue Service (SQS)
Standard and FIFO queues
Message delivery and visibility timeout
Dead-letter queues
Amazon Simple Notification Service (SNS)
Fan-out messaging patterns
Amazon EventBridge
Event buses, rules, and targets
Retry handling and failure management
Message-driven application patterns
What you'll build
- Design an order-processing workflow in which an application places a message on an SQS queue and a separate worker processes it.
By the end: Learners understand how messaging and event services help applications communicate asynchronously and handle workloads reliably.
Understand containerization and the AWS services used to run containerized applications.
Topics covered
Introduction to containers
Virtual machines versus containers
Docker images and containers
Dockerfiles and image building
Container registries
Amazon Elastic Container Registry (ECR)
Container networking and environment configuration
Container logs and health checks
Amazon Elastic Container Service (ECS)
AWS Fargate overview
Amazon Elastic Kubernetes Service (EKS) overview
ECS versus EKS at a high level
Container security and image management
Container deployment considerations
What you'll build
- Package a simple web application into a Docker container and identify how its image could be stored in ECR and deployed using ECS or Fargate.
By the end: Learners understand containerization fundamentals and the AWS services used to build, store, and run containers.
Understand how cloud infrastructure can be provisioned and managed consistently through configuration and automation.
Topics covered
Manual infrastructure configuration versus Infrastructure as Code (IaC)
Benefits of repeatable infrastructure provisioning
AWS CloudFormation fundamentals
Templates, stacks, and resources
Parameters, outputs, and resource dependencies
Stack updates and deletion
Introduction to AWS CDK
Configuration management fundamentals
AWS Systems Manager overview
Automation documents and Run Command
Basic scripting for cloud administration
Infrastructure change review and rollback considerations
What you'll build
- Review or create a basic CloudFormation template that provisions a simple AWS resource and examine how a stack can be updated.
By the end: Learners understand the fundamentals of infrastructure automation and repeatable resource provisioning.
Understand how software changes can be tested and deployed through automated delivery pipelines.
Topics covered
Software development lifecycle in cloud projects
Version control fundamentals with Git
Source code repositories
Continuous Integration and Continuous Delivery/Deployment
Build, test, and deployment stages
AWS CodeBuild overview
AWS CodePipeline overview
AWS CodeDeploy overview
GitHub Actions as an alternative CI/CD tool
Build artifacts and deployment environments
Automated testing in delivery pipelines
Deployment approvals and rollback
Secure handling of credentials in pipelines
What you'll build
- Design a CI/CD pipeline that retrieves application code, runs tests, builds a deployable artifact, and releases it to a test environment.
By the end: Learners understand the main stages of a CI/CD pipeline and how automated delivery improves consistency and release reliability.
Understand how to estimate, monitor, and optimize cloud expenditure.
Topics covered
AWS pricing fundamentals
On-demand, reserved, and savings-based purchasing options at a high level
Pay-as-you-go billing
AWS Pricing Calculator
AWS Cost Explorer
AWS Budgets
Cost allocation tags
Identifying idle and underutilized resources
EC2 instance sizing
Storage lifecycle and retention policies
Data transfer costs
Cost-aware architecture decisions
Budget alerts and spending controls
Basic FinOps principles
What you'll build
- Estimate the monthly cost of a simple web application using EC2, S3, and RDS, then identify practical ways to reduce unnecessary spending.
By the end: Learners understand the major sources of AWS costs and can make basic cost estimates and optimization recommendations.
Understand how to design cloud architectures that meet availability, reliability, performance, and security requirements.
Topics covered
Fundamentals of cloud architecture design
AWS Well-Architected Framework overview
Operational excellence
Security
Reliability
Performance efficiency
Cost optimization
Sustainability
Single Availability Zone versus Multi-AZ architecture
Stateless and stateful application components
Load balancing and Auto Scaling
Managed databases and storage selection
Fault tolerance and failure handling
Architectural trade-offs
Basic architecture diagrams and documentation
What you'll build
- Design a highly available web application using a load balancer, multiple Availability Zones, application instances, and a managed database.
By the end: Learners understand core cloud architecture principles and can explain design choices for a reliable application.
Understand AWS services used to store, process, transform, and analyse data.
Topics covered
Introduction to cloud-based data processing
Data lakes and data warehouses
Amazon S3 as a data lake storage layer
AWS Glue overview
Data catalog and ETL fundamentals
Amazon Athena for SQL queries over supported data sources
Amazon Redshift overview
Batch versus streaming data processing
Amazon Kinesis overview
Data ingestion and transformation workflows
Data access and permissions
Storage and query cost considerations
What you'll build
- Design a simple analytics workflow in which data is stored in S3, catalogued for discovery, and queried using an appropriate AWS analytics service.
By the end: Learners understand the purpose of key AWS analytics services and how they can be combined to support data processing and reporting.
Understand the basic approaches for moving applications and data to AWS and connecting cloud infrastructure with existing environments.
Topics covered
Introduction to cloud migration
Reasons for migrating to the cloud
Application and infrastructure assessment
The 6 Rs of migration at a high level
Rehosting, replatforming, refactoring, repurchasing, retaining, and retiring
Migration planning and dependency analysis
Data migration considerations
Hybrid cloud concepts
AWS Site-to-Site VPN overview
AWS Direct Connect overview
AWS migration and transfer service overview
Downtime and business continuity considerations
Migration validation and post-migration testing
What you'll build
- Prepare a basic migration plan for a company moving a small web application and its database from an on-premises environment to AWS.
By the end: Learners understand common migration strategies and the factors that influence cloud migration planning.
Develop a systematic approach to identifying and resolving common AWS infrastructure and application issues.
Topics covered
Common cloud infrastructure failures
EC2 connectivity and instance status checks
Security group and network ACL troubleshooting
VPC route and internet access issues
DNS resolution problems
S3 access and permission errors
Database connectivity issues
IAM access-denied errors
CloudWatch metrics and log investigation
AWS CloudTrail event analysis
Resource health checks
Incident investigation and escalation
Root-cause analysis fundamentals
Operational documentation and runbooks
What you'll build
- Analyse a simulated cloud application failure, identify the likely issue using logs and configuration details, and document the troubleshooting steps and resolution.
By the end: Learners can follow a structured troubleshooting process for common networking, access, compute, storage, and database issues.
Apply the concepts covered in the course to design, deploy, secure, monitor, and document a cloud-based application.
Topics covered
Gathering application requirements
Selecting appropriate AWS services
Preparing the architecture diagram
Designing the VPC and network access
Configuring IAM permissions
Deploying application infrastructure
Integrating storage and database services
Configuring load balancing where required
Implementing monitoring and logging
Applying backup and security controls
Estimating and reviewing costs
Testing application availability and access
Documenting deployment and recovery procedures
Preparing a final project demonstration
What you'll build
- Build and deploy a cloud-hosted web application using suitable AWS services. A possible architecture includes a VPC, EC2, IAM, S3, RDS, a load balancer, CloudWatch, and a documented backup strategy. More advanced learners can use containers, Infrastructure as Code, or CI/CD.
By the end: Learners complete a practical cloud project and demonstrate their understanding of AWS infrastructure, security, networking, deployment, monitoring, and cost management.
Program 3 — Industry Project Engineering Standards (24 modules)
The complete professional software engineering curriculum — from requirements through deployment, monitoring, and client handover. Every module produces a real deliverable. By the end, you have shipped a complete application the way a consulting firm would: requirements signed off, code tested, system documented, and ownership transferred.
Understand how cloud computing is used in enterprise applications and how cloud infrastructure supports real client projects.
Topics covered
Cloud computing concepts and service models.
Infrastructure as a Service (IaaS).
Platform as a Service (PaaS).
Software as a Service (SaaS).
Public, private, hybrid, and multi-cloud environments.
Scalability, elasticity, availability, and fault tolerance.
Cloud deployment models and workload characteristics.
Capital expenditure versus operational expenditure.
Cloud-native and traditional application architectures.
Responsibilities of cloud providers and customers.
Shared responsibility model.
Cloud governance and resource ownership.
Introduction to AWS global infrastructure.
AWS Regions, Availability Zones, and edge locations.
Enterprise cloud adoption and migration considerations.
Technologies and Tools
Amazon Web Services.
AWS Management Console.
AWS CLI.
Git.
Architecture diagramming tools.
What you'll build
- Analyse a traditional business application running on a physical server and propose a cloud deployment approach.
- Identify the application components, required cloud services, security considerations, availability requirements, and potential operational benefits.
By the end: Participants should understand how cloud infrastructure supports enterprise applications and how to identify appropriate cloud services for a business requirement.
Learn how to understand client expectations and translate business problems into technical requirements before selecting technologies.
Topics covered
Understanding the client's business domain.
Identifying stakeholders and end users.
Conducting technical discovery meetings.
Identifying current business problems.
Understanding existing applications and infrastructure.
Functional and non-functional requirements.
Application workflows and user journeys.
Data sources and integration dependencies.
What you'll build
- Assume a client wants to build an online inventory management system.
- Prepare a discovery document covering users, business workflows, inventory operations, database requirements, security expectations, integration requirements, expected traffic, and deployment constraints.
By the end: Participants should be able to collect structured requirements and identify the technical information needed before beginning cloud architecture and development.
Learn to convert client requirements into implementation-ready specifications that developers, testers, architects, and operations teams can follow.
Topics covered
Understanding a Software Requirements Specification (SRS).
Understanding a Product Requirements Document (PRD).
Functional requirements.
Non-functional requirements.
User stories and acceptance criteria.
Business rules and validation requirements.
System workflows and use cases.
Data flow and integration requirements.
API requirements.
Performance and scalability requirements.
Security and access requirements.
Deployment and operational requirements.
Requirement prioritisation.
Requirement traceability matrices.
Version control for project documentation.
Change requests and impact analysis.
Technical feasibility analysis.
Identifying missing or contradictory requirements.
Required Project Deliverables
SRS or equivalent requirements specification.
PRD or product scope document.
User stories and acceptance criteria.
Functional and non-functional requirements list.
Initial system architecture diagram.
API and integration requirements.
Risk and assumption register.
Requirement traceability matrix.
What you'll build
- Convert an inventory management client's requirements into an SRS and PRD.
- Create user stories for inventory creation, stock updates, supplier management, user authentication, and reporting.
- Link each requirement to its corresponding implementation component and test case.
By the end: SRS or equivalent requirements specification. PRD or product scope document. User stories and acceptance criteria. Functional and non-functional requirements list. Initial system architecture diagram. API and integration requirements. Risk and assumption register. Requirement traceability matrix.
Learn to design cloud architectures that meet client requirements for functionality, security, scalability, reliability, maintainability, and cost.
Topics covered
Monolithic architecture.
Modular monolithic architecture.
Microservices architecture.
Event-driven architecture.
Serverless architecture.
Three-tier architecture.
API-based architecture.
Synchronous versus asynchronous communication.
Stateless and stateful application components.
Horizontal and vertical scaling.
High availability and fault tolerance.
Database architecture.
Caching strategies.
Network architecture.
Cloud security boundaries.
Disaster recovery architecture.
Architecture trade-offs.
Architecture Decision Records (ADRs).
AWS Well-Architected Framework concepts.
Cost estimation and operational complexity.
Technology Selection Criteria
Technology selection should consider:
Client requirements.
Existing technology stack.
Team expertise.
Workload and traffic characteristics.
Security and compliance.
Availability and recovery targets.
Performance requirements.
Integration compatibility.
Licensing and operational costs.
Long-term maintenance.
Vendor and platform constraints.
Example Technology Mapping
| Requirement | Possible Technology |
| ----- | ----- |
| Virtual machine hosting | Amazon EC2 |
| Containerised applications | Amazon ECS or Amazon EKS |
| Event-driven execution | AWS Lambda |
| Managed relational database | Amazon RDS or Aurora |
| Key-value access patterns | Amazon DynamoDB |
| Object and document storage | Amazon S3 |
| API management | Amazon API Gateway |
| Application load balancing | Elastic Load Balancing |
| DNS management | Amazon Route 53 |
| Content delivery | Amazon CloudFront |
| Monitoring and alarms | Amazon CloudWatch |
| Infrastructure automation | Terraform or AWS CloudFormation |
What you'll build
- Design two possible architectures for an online booking application:
- A conventional application using managed compute and a relational database.
- An event-driven application using serverless services.
- Compare their security, cost, scalability, reliability, and operational complexity.
By the end: Participants should be able to explain and justify technology choices rather than selecting cloud services without considering project requirements.
Learn to establish secure cloud environments with controlled access and appropriate governance.
Topics covered
AWS account structure.
AWS Organizations.
Multi-account environment strategies.
AWS IAM users, groups, roles, and policies.
Identity federation and single sign-on.
Role-based access control.
Least-privilege access.
Temporary credentials.
Workload identities and service roles.
Cross-account access.
Privileged access management.
AWS IAM Identity Center.
AWS Organizations Service Control Policies.
AWS CloudTrail.
AWS Config.
Resource tagging and ownership.
Environment separation.
Access reviews.
Emergency access procedures.
Cloud governance policies.
What you'll build
- Create an appropriate development and deployment access model for a sample project.
- Define separate permissions for developers, testers, deployment automation, and administrators.
- Review the permissions to identify excessive privileges.
By the end: Participants should understand how to manage cloud access, separate responsibilities, and establish an auditable cloud environment.
Understand how enterprise applications communicate securely within cloud environments and with external systems.
Topics covered
Networking fundamentals.
IP addresses and CIDR notation.
Public and private IP addresses.
Subnet design.
Amazon VPC.
Public and private subnets.
Route tables.
Internet gateways.
NAT gateways.
Security groups.
Network ACLs.
VPC endpoints.
VPC peering.
AWS Transit Gateway.
Site-to-Site VPN.
AWS Direct Connect.
DNS resolution.
Private connectivity.
Network segmentation.
Load balancers.
Network troubleshooting.
VPC Flow Logs.
What you'll build
- Create a VPC architecture containing:
- Public subnets for internet-facing components where required.
- Private application subnets.
- Private database subnets.
- Appropriate routing.
- Security groups restricting access between tiers.
- Required internet or private service connectivity.
- Validate that only approved communication paths are available.
By the end: Participants should be able to design and troubleshoot secure cloud networking for multi-tier applications.
Understand how to select and configure suitable compute services for different application workloads.
Topics covered
Amazon EC2.
Instance types and sizing.
Amazon Machine Images.
Key pairs and secure access.
EBS volumes.
EC2 security groups.
EC2 user data.
Auto Scaling groups.
Application Load Balancer.
Network Load Balancer.
Health checks.
Launch templates.
Instance patching and hardening.
Amazon ECS.
AWS Fargate.
Amazon EKS.
AWS Lambda.
AWS App Runner where appropriate.
Compute cost management.
High availability.
Deployment strategies.
Technology Selection Guidance
EC2:** Appropriate when the application needs operating-system-level control, specialised software, or a traditional server environment.
ECS with Fargate:** Suitable for many containerised applications when the team wants managed container infrastructure without managing worker nodes directly.
EKS:** Appropriate when Kubernetes capabilities, ecosystem compatibility, or organisational platform standards justify the additional complexity.
Lambda:** Suitable for supported event-driven workloads and functions where managed execution and automatic scaling are beneficial.
Managed application platforms:** Useful when simplifying application deployment is more important than managing underlying infrastructure directly.
What you'll build
- Deploy a sample web application using an appropriate compute service.
- Configure health checks, application logging, access restrictions, and a repeatable deployment procedure.
By the end: Participants should be able to select, configure, and operate compute services based on workload requirements.
Learn to design reliable and secure data storage for cloud applications.
Topics covered
Storage requirements analysis.
Amazon S3.
S3 buckets and object permissions.
S3 versioning.
S3 lifecycle policies.
S3 encryption.
S3 replication.
Amazon EBS.
Amazon EFS.
Amazon FSx where required.
Amazon RDS.
Amazon Aurora.
Amazon DynamoDB.
Database selection criteria.
Relational versus non-relational databases.
Database schema design.
Indexing and query optimisation.
Connection pooling.
Database security.
Encryption and secrets management.
Backup and restoration.
Point-in-time recovery.
Data migration.
Data retention and deletion.
Database monitoring.
What you'll build
- For a sample inventory application:
- Design the relational database schema.
- Deploy an appropriate managed database.
- Connect the application securely.
- Store uploaded documents in S3.
- Configure database backups.
- Test database restoration.
- Validate application behaviour after recovery.
By the end: Participants should be able to select appropriate storage technologies, protect business data, and implement backup and recovery procedures.
Develop backend services that integrate securely with cloud infrastructure and expose reliable application functionality.
Topics covered
Backend application architecture.
REST API principles.
Request and response handling.
Authentication and authorisation.
Input validation.
Exception handling.
API versioning.
Pagination and filtering.
Database integration.
Connection management.
Configuration management.
Logging.
Health endpoints.
Timeouts and retries.
Idempotency.
External API integration.
Application packaging.
Environment configuration.
Unit and integration testing.
Framework Selection
Python
FastAPI for API-oriented applications.
Django for applications that benefit from its integrated web framework features.
Pytest for automated testing.
Pydantic for data validation where appropriate.
Java
Spring Boot for enterprise backend applications.
Spring Security for authentication and authorisation.
Spring Data for supported persistence patterns.
JUnit for testing.
Maven or Gradle for build management.
JavaScript and TypeScript
Node.js with a suitable approved backend framework.
TypeScript for type safety.
Automated testing frameworks appropriate to the chosen stack.
What you'll build
- Build a backend service for a business workflow.
- Implement CRUD operations, validation, database access, error handling, API documentation, and automated tests.
- Deploy it to an appropriate AWS compute service.
By the end: Participants should be able to develop, test, package, and deploy backend applications using a framework suitable for the client project.
Understand how cloud applications expose APIs, communicate across services, and handle asynchronous business processes.
Topics covered
REST API design.
OpenAPI specifications.
Amazon API Gateway.
Application Load Balancer routing.
API authentication and authorisation.
API throttling.
Rate limiting.
Request validation.
API versioning.
Microservices communication.
Amazon SQS.
Amazon SNS.
Amazon EventBridge.
AWS Step Functions.
Event-driven architecture.
Message retries.
Dead-letter queues.
Duplicate message handling.
Idempotent processing.
Distributed transaction considerations.
Integration testing.
Service-to-service security.
What you'll build
- Build an order-processing workflow in which:
- An order is submitted through an API.
- The order is validated and persisted.
- An event or message is generated.
- A background worker processes the order.
- Failures are retried according to policy.
- Unprocessable messages are routed to an appropriate failure destination.
- Monitoring records processing errors.
By the end: Participants should be able to design API integrations and asynchronous workflows that remain reliable under load and recover from failures.
Learn to provision and maintain cloud infrastructure through version-controlled, repeatable automation.
Topics covered
Infrastructure as Code concepts.
Terraform.
AWS CloudFormation.
AWS Cloud Development Kit.
Reusable infrastructure modules.
Input variables and outputs.
Environment-specific configuration.
Terraform state management.
Remote state and state locking where supported.
Resource dependencies.
Infrastructure validation.
Plan and change-set review.
Drift detection.
Policy as code.
Secrets handling.
Infrastructure testing.
Automated provisioning.
Resource tagging.
Infrastructure versioning.
Safe destruction and recovery procedures.
Tool Selection
Choose one primary infrastructure-as-code approach based on the client's existing standards.
Terraform may be suitable for teams requiring a provider-based infrastructure workflow.
CloudFormation provides AWS-native infrastructure management.
AWS CDK allows infrastructure definitions using supported programming languages.
Do not introduce multiple competing infrastructure tools without a clear reason.
What you'll build
- Create reusable infrastructure definitions for a sample project containing:
- A VPC and appropriate subnets.
- Security groups.
- Application compute.
- A managed database.
- Logging and monitoring.
- Required IAM roles.
- Review the proposed infrastructure changes before applying them.
By the end: Participants should be able to automate cloud infrastructure provisioning and reduce configuration differences between environments.
Understand container-based application packaging and deployment in cloud environments.
Topics covered
Containerisation concepts.
Docker images and containers.
Dockerfiles.
Container registries.
Amazon ECR.
Image tagging and versioning.
Container networking.
Environment variables and secrets.
Container health checks.
Resource limits.
Image scanning.
Container security.
Docker Compose for local development where appropriate.
Amazon ECS.
AWS Fargate.
Amazon EKS.
Kubernetes pods, deployments, and services.
Kubernetes configuration and secrets.
Ingress and load balancing.
Autoscaling.
Container logging.
Rolling updates and rollback.
What you'll build
- Containerise an existing backend application.
- Build the image, test it locally, push it to a registry, deploy it to the selected AWS container platform, and configure health checks and monitoring.
By the end: Participants should understand how to package applications consistently and select a suitable container platform without introducing unnecessary operational complexity.
Build automated software delivery pipelines that improve release consistency, traceability, and deployment safety.
Topics covered
Git fundamentals.
Branching strategies.
Pull requests and code reviews.
Build automation.
Dependency management.
Continuous Integration.
Continuous Delivery and Deployment.
GitHub Actions.
GitLab CI/CD.
Jenkins.
AWS deployment services where appropriate.
Automated testing.
Static code analysis.
Dependency scanning.
Secret scanning.
Container image scanning.
Infrastructure-as-code scanning.
Artefact repositories.
Environment promotion.
Deployment approvals.
Release tagging.
Rollback procedures.
Deployment audit trails.
Standard Pipeline
A typical pipeline should include:
Source checkout.
Dependency installation.
Formatting and linting.
Unit tests.
Static code and dependency checks.
Secret and infrastructure scanning.
Application build.
Container image build where applicable.
Artefact storage.
Deployment to a test environment.
Integration and smoke tests.
Approval where required.
Production deployment.
Post-deployment health verification.
Release evidence and rollback readiness.
What you'll build
- Build a pipeline that automatically tests an application and deploys it to a non-production environment after approved changes are merged.
- Introduce a failing test and verify that the pipeline prevents the release from proceeding.
By the end: Participants should be able to develop controlled CI/CD pipelines and understand how to prevent defective or insecure changes from reaching production.
Implement security controls throughout the cloud application lifecycle.
Topics covered
Shared responsibility model.
Security architecture.
Threat modelling.
IAM least privilege.
Workload identity.
Network security.
Encryption at rest and in transit.
AWS KMS.
AWS Secrets Manager.
AWS Systems Manager Parameter Store.
AWS WAF.
AWS Shield.
AWS CloudTrail.
AWS Config.
Vulnerability management.
Dependency scanning.
Container security.
Application security testing.
Secure coding practices.
Data classification.
Privacy and retention.
Audit logging.
Security incident response.
Risk assessment and exception management.
Security Engineering Requirements
Every project should identify:
Data sensitivity and access requirements.
Trust boundaries.
Publicly exposed components.
Authentication and authorisation controls.
Secret storage and rotation procedures.
Encryption requirements.
Vulnerability remediation processes.
Audit and monitoring requirements.
Incident escalation procedures.
Applicable client and regulatory controls.
What you'll build
- Review a sample cloud architecture and identify security weaknesses, such as excessive permissions, public database access, exposed secrets, missing encryption, or insufficient logging.
- Propose corrective changes and validate them using approved security checks.
By the end: Participants should be able to identify common cloud security risks and implement appropriate controls throughout development and deployment.
Understand how to detect application failures, identify performance problems, and monitor production cloud environments.
Topics covered
Monitoring versus observability.
Amazon CloudWatch.
CloudWatch metrics.
CloudWatch Logs.
CloudWatch dashboards.
CloudWatch alarms.
AWS CloudTrail.
AWS Config.
Distributed tracing.
AWS X-Ray where appropriate.
OpenTelemetry where appropriate.
Structured logging.
Correlation and trace IDs.
Application performance monitoring.
Error rates and latency.
Infrastructure utilisation.
Queue depth and processing failures.
Alert thresholds.
Notification and escalation.
Log retention and redaction.
Troubleshooting from telemetry.
What you'll build
- Deploy a cloud application and configure:
- Application and infrastructure metrics.
- Centralised logs.
- A dashboard.
- Alerts for application errors and resource saturation.
- A notification or incident-routing process.
- Generate a controlled failure and investigate the resulting telemetry.
By the end: Participants should be able to monitor cloud workloads and use evidence from metrics, logs, and traces to diagnose operational problems.
Design recovery and resilience mechanisms that meet agreed business continuity requirements.
Topics covered
High availability.
Fault tolerance.
Availability Zones.
Multi-AZ architecture.
Load balancing.
Auto Scaling.
Backup strategies.
Database backup and restoration.
Amazon S3 versioning and replication.
Point-in-time recovery.
Cross-region recovery where required.
Recovery Time Objective (RTO).
Recovery Point Objective (RPO).
Disaster recovery strategies.
Failover procedures.
Recovery testing.
Business continuity.
Incident communication.
Data integrity validation.
What you'll build
- Create a recovery plan for a sample application.
- Document its RTO and RPO, configure appropriate backups, restore a backup in a controlled environment, and verify that the application and its data are usable.
By the end: Participants should be able to implement and validate recovery procedures rather than relying only on configured backups.
Optimise cloud application performance and manage infrastructure costs without compromising required security or reliability.
Topics covered
Performance baselining.
Latency and throughput.
Load and stress testing.
Application profiling.
Database query optimisation.
Connection pooling.
Caching.
Horizontal and vertical scaling.
Auto Scaling configuration.
Capacity planning.
Queue-based load management.
CloudWatch performance metrics.
AWS pricing models.
AWS Budgets.
Cost allocation tags.
AWS Cost Explorer.
Storage lifecycle management.
Idle resource identification.
Data transfer costs.
Resource rightsizing.
Reserved capacity and savings options where appropriate.
Cost forecasting and reporting.
What you'll build
- Deploy a sample application, establish a performance baseline, and simulate increased traffic.
- Identify bottlenecks, propose scaling improvements, estimate their cost, and compare the performance and cost before and after optimisation.
By the end: Participants should be able to make evidence-based performance improvements and explain the financial impact of cloud architecture decisions.
Understand how organisations move existing applications and data to the cloud while controlling risk and downtime.
Topics covered
Application and infrastructure discovery.
Dependency mapping.
Migration readiness assessment.
Cloud suitability analysis.
Migration planning.
Data migration.
Application migration.
Network and identity dependencies.
The 6 Rs of migration:
Rehost.
Replatform.
Refactor or re-architect.
Repurchase.
Retire.
Retain.
Migration tools and AWS migration services.
Database migration considerations.
Data validation.
Cutover planning.
Downtime management.
Rollback planning.
Post-migration validation.
Performance and cost comparison.
Legacy application modernisation.
What you'll build
- Analyse a sample application hosted on an on-premises server.
- Recommend a migration approach, identify dependencies, design the target AWS architecture, prepare a migration sequence, and define validation and rollback procedures.
By the end: Participants should be able to prepare a practical cloud migration plan based on application constraints and business priorities.
Develop a structured approach to investigating cloud infrastructure, application, networking, deployment, and security issues.
Topics covered
Incident identification and classification.
Application errors.
Failed deployments.
Network connectivity problems.
IAM permission failures.
Database connection issues.
Resource exhaustion.
Scaling failures.
DNS and TLS problems.
Container startup failures.
Lambda timeout and concurrency issues.
Queue backlogs.
CloudWatch investigation.
CloudTrail investigation.
Root-cause analysis.
Log correlation.
Rollback and recovery.
Incident communication.
Post-incident reviews.
Corrective and preventive actions.
Troubleshooting Method
Define the observed problem and user impact.
Identify when the problem started.
Review recent deployments and configuration changes.
Collect logs, metrics, traces, and relevant events.
Form and test possible root-cause hypotheses.
Stabilise the service using an approved mitigation.
Verify service recovery.
Document the root cause and contributing factors.
Create follow-up actions to prevent recurrence.
What you'll build
- Investigate a simulated outage caused by a misconfigured security group, missing IAM permission, failed deployment, or unavailable database.
- Document the evidence, diagnosis, resolution, and prevention steps.
By the end: Participants should be able to troubleshoot cloud failures systematically and produce useful incident reports.
Establish the technical and operational controls required before a cloud application is accepted for production use.
Topics covered
Architecture review.
Security review.
Operational readiness.
Capacity and performance assessment.
Reliability assessment.
Cost review.
Data protection review.
Access review.
Compliance evidence.
Backup and recovery readiness.
Monitoring and alert readiness.
Deployment validation.
Rollback planning.
Documentation review.
Service ownership.
Support escalation.
Change approval.
Production go/no-go decisions.
Production Readiness Checklist
Before launch, verify that:
Requirements and acceptance criteria have been approved.
Critical functional and security defects have been resolved.
Architecture and security reviews are complete.
Infrastructure is reproducible through approved automation.
Secrets and permissions follow the security policy.
Automated tests and required quality checks pass.
Monitoring, logging, and alerts are configured.
Backup and restoration procedures have been tested.
RTO and RPO are documented and approved.
Deployment and rollback procedures are available.
Cloud cost and capacity have been reviewed.
Runbooks and support ownership are defined.
Client approvals and release evidence are recorded.
What you'll build
- Conduct a mock production-readiness review for a cloud-hosted application.
- Identify gaps, classify risks, assign owners, and determine whether the application is ready for release.
By the end: Participants should understand the evidence and approvals required before delivering a cloud solution to a client.
Use approved AI tools to accelerate cloud engineering tasks while maintaining security, correctness, code quality, and human accountability. The objective is to identify tasks where AI can reduce repetitive work, improve investigation speed, and help engineers deliver project outcomes faster. Productivity improvements must be measured rather than assumed.
Topics covered
AI-assisted requirement analysis.
AI-assisted SRS and PRD review.
AI-assisted architecture brainstorming.
AI-assisted repository exploration.
AI-assisted AWS service comparison.
AI-assisted Terraform and CloudFormation drafting.
AI-assisted application code generation.
AI-assisted API implementation.
AI-assisted unit and integration test generation.
AI-assisted debugging.
AI-assisted log analysis.
AI-assisted security review.
AI-assisted CI/CD pipeline development.
AI-assisted technical documentation.
AI-assisted migration planning.
AI-assisted release checklists.
AI-assisted incident report drafting.
AI output verification.
Secure use of AI tools with client data.
Human review and approval controls.
Example Tools
Depending on client approval and availability:
Google Antigravity.
Claude and Claude Code.
GitHub Copilot.
Other approved AI coding assistants.
Approved documentation and knowledge-search tools.
Tool capabilities, licensing, data-handling terms, and available features should be verified before adoption.
What you'll build
- Give participants an existing cloud project repository and a clearly scoped requirement.
- Ask them to use an approved AI assistant to:
- Inspect the repository and summarise the architecture.
- Identify the files and components likely to change.
- Draft an implementation plan.
- Implement one small feature.
- Generate or update tests.
- Run the tests and security checks.
- Review the code diff manually.
- Update the relevant documentation.
- Report what changed, what was tested, and what remains uncertain.
By the end: Participants should be able to use AI as an engineering assistant across the project lifecycle without treating generated code, architecture advice, or security recommendations as automatically correct.
Prepare the documentation and evidence required for another engineering team or client to operate, maintain, and extend the delivered solution.
Topics covered
Technical documentation standards.
Architecture diagrams.
Deployment diagrams.
Network diagrams.
Data flow diagrams.
API documentation.
Database documentation.
Environment setup guides.
Infrastructure provisioning instructions.
CI/CD documentation.
Configuration management.
Troubleshooting guides.
Operations runbooks.
Backup and recovery procedures.
Security and access documentation.
Release notes.
Known limitations.
Incident history.
Support ownership.
Client demonstration.
Knowledge-transfer sessions.
Handover acceptance.
Suggested Repository Structure
cloud-project/
├── docs/
│ ├── SRS.md
│ ├── PRD.md
│ ├── architecture.md
│ ├── deployment.md
│ ├── security.md
│ └── runbook.md
├── application/
│ ├── src/
│ └── tests/
├── infrastructure/
│ ├── modules/
│ └── environments/
├── scripts/
├── tests/
├── .github/
│ └── workflows/
├── README.md
└── CHANGELOG.md
This is a reference structure. Adapt it to the selected language, framework, repository conventions, and client requirements.
What you'll build
- Prepare a handover package for a completed cloud application.
- Include deployment instructions, architecture diagrams, environment configuration guidance, monitoring instructions, recovery procedures, known issues, and support responsibilities.
By the end: Participants should be able to deliver a maintainable solution with sufficient documentation for client acceptance and ongoing operations.
Apply the technologies and engineering practices from the previous modules to a complete client-style cloud project.
Topics covered
By the end: Participants should demonstrate that they can deliver a cloud-based application through a complete engineering lifecycle, not merely deploy a service from the AWS Console.
Evaluate whether participants can deliver cloud solutions using professional engineering practices and meet agreed project acceptance criteria.
Topics covered
By the end: The final project submission should contain: Approved SRS or equivalent requirements specification. PRD and acceptance criteria. Architecture and data flow diagrams. Technology selection rationale. Source code repository. Infrastructure-as-code repository. Automated tests and test results. CI/CD pipeline configuration. Security review and relevant scan evidence. Deployment instructions. Monitoring and alert configuration. Backup and recovery evidence. Cost estimate. Operations runbook. Known issues and risk register. Release record. Demonstration and handover notes.
Program 4 — Leveraging AI for 10× Faster Software Development (31 modules)
31 hands-on modules that take you through the complete development lifecycle — from requirement gathering to deployment — using AI coding tools at every stage. All premium tools (ChatGPT-4, Gemini Advanced, GitHub Copilot, Cursor, and more) are configured free on your laptop. Value: ₹15,000+. You use them throughout the program, not just here — and they stay yours.
Learn to write effective prompts that help AI assist with cloud architecture, infrastructure setup, deployment, troubleshooting, and automation.
Topics covered
Fundamentals of prompt engineering for cloud development.
Writing role-based prompts for cloud architects, DevOps engineers, and cloud developers.
Generating cloud CLI commands and configuration files using AI.
Creating prompts for AWS, Microsoft Azure, and Google Cloud Platform.
Using AI to generate infrastructure-as-code templates.
Improving prompts through iterative testing and refinement.
Validating AI-generated commands and configurations before execution.
What you'll build
- Create reusable prompts for common cloud development tasks.
- Use AI to generate AWS CLI commands for creating and managing cloud resources.
- Generate a basic Terraform configuration using AI.
- Improve a prompt to obtain a more secure and cost-effective cloud deployment plan.
By the end: Develop reusable prompts for cloud development tasks. Reduce time spent searching for commands, writing configurations, and solving routine problems. Use AI effectively while validating commands before applying them to real environments.
Learn how AI can analyze existing cloud project files, configurations, architecture documents, and deployment scripts to build project context and reduce manual investigation.
Topics covered
Understanding cloud project structure and dependencies.
Using AI to analyze Terraform files, Dockerfiles, YAML files, and deployment scripts.
Extracting information from cloud architecture diagrams and documentation.
Identifying cloud services, resource dependencies, and environment configurations.
Generating project summaries and setup instructions.
Creating reusable project-context documents for AI-assisted development.
Protecting credentials, API keys, and confidential infrastructure information when using AI tools.
What you'll build
- Provide a sample cloud project structure and ask AI to explain each component.
- Analyze Terraform files to identify resources and dependencies.
- Generate a cloud architecture summary from project documentation.
- Create a project-context document containing services, configurations, dependencies, and deployment steps.
By the end: Understand unfamiliar cloud projects more quickly. Reduce time spent manually exploring configuration files and documentation. Build reusable project context for future cloud development and troubleshooting.
Learn how AI can analyze a Software Requirements Specification (SRS) and identify cloud infrastructure, deployment, security, availability, and scalability requirements.
Topics covered
Understanding SRS documents for cloud-based applications.
Extracting functional and non-functional requirements.
Identifying cloud hosting, storage, networking, and database requirements.
Analyzing availability, scalability, performance, backup, and disaster recovery requirements.
Identifying security requirements involving identity management, access control, and encryption.
Mapping requirements to suitable cloud services.
Detecting missing, ambiguous, or conflicting requirements.
Generating requirement traceability matrices using AI.
What you'll build
- Analyze an SRS for a cloud-based employee management application.
- Extract requirements for compute, storage, networking, and databases.
- Identify missing requirements related to security, monitoring, backup, and availability.
- Generate a requirements checklist and traceability matrix.
By the end: Convert lengthy SRS documents into actionable cloud requirements. Identify infrastructure gaps before implementation begins. Reduce manual requirement-analysis effort and improve planning accuracy.
Learn how AI can convert an SRS into a Product Requirements Document (PRD) that defines cloud application features, infrastructure expectations, deployment requirements, and acceptance criteria.
Topics covered
Understanding the differences between SRS and PRD.
Extracting user needs, business goals, and application features from an SRS.
Defining cloud application capabilities and expected user workflows.
Documenting infrastructure, availability, scalability, and performance expectations.
Defining security, monitoring, backup, and disaster recovery requirements.
Creating functional requirements and measurable acceptance criteria.
Generating implementation priorities and milestone plans.
Reviewing AI-generated PRDs for completeness and technical feasibility.
What you'll build
- Convert an SRS for a cloud-based document management system into a PRD.
- Define application features and cloud infrastructure expectations.
- Generate acceptance criteria for deployment, access control, and application availability.
- Review the PRD against the original SRS to identify missing requirements.
By the end: Generate structured PRDs more quickly. Establish a clearer connection between business requirements and cloud implementation. Reduce repetitive documentation work and improve project planning.
Learn how AI can identify unclear cloud requirements and generate focused questions to clarify infrastructure, security, cost, performance, and deployment expectations with clients.
Topics covered
Identifying ambiguous requirements in cloud projects.
Generating client questions from SRS and PRD documents.
Clarifying cloud provider preferences and hosting requirements.
Understanding expected traffic, workload size, and scalability needs.
Gathering requirements for availability, backup, recovery, and monitoring.
Clarifying data residency, access control, compliance, and security expectations.
Estimating budget constraints and identifying potential cost risks.
Preparing client meeting agendas and requirement-confirmation documents.
What you'll build
- Analyze a cloud migration requirement and generate clarification questions.
- Prepare questions for a client planning to host an e-commerce application in the cloud.
- Identify missing details about traffic, database size, uptime, backup, and budget.
- Generate a client requirement checklist and meeting summary template.
By the end: Identify missing information before cloud implementation starts. Reduce misunderstandings between clients and technical teams. Improve infrastructure planning, cost estimation, and deployment decisions.
Learn how AI can assist in designing cloud architectures by recommending suitable services, generating architecture diagrams, proposing infrastructure configurations, and identifying security and scalability risks.
Topics covered
Fundamentals of cloud architecture and system design.
Using AI to recommend suitable cloud services based on requirements.
Designing compute, storage, database, networking, and application layers.
Generating architecture diagrams and infrastructure documentation.
Creating basic Terraform or CloudFormation templates using AI.
Designing scalable and highly available cloud architectures.
Identifying security risks and applying least-privilege access principles.
Evaluating cost, performance, reliability, and maintainability.
Reviewing AI-generated architecture against requirements and cloud best practices.
What you'll build
- Design a cloud architecture for an employee management application using AWS, Azure, or GCP.
- Ask AI to recommend compute, database, storage, and networking services.
- Generate a basic architecture diagram and infrastructure-as-code template.
- Review the design for security, availability, scalability, and cost.
- Compare two proposed architectures and document their trade-offs.
By the end: Develop cloud architecture proposals more efficiently. Accelerate infrastructure planning and configuration generation. Understand the trade-offs between cost, performance, security, and reliability. Produce architecture documents and initial infrastructure templates for review.
Enable engineers to use AI to understand complex corporate project requirements, existing systems, technical documentation, and business objectives before beginning implementation.
Topics covered
Understanding real-world enterprise cloud project requirements
Analyzing project SRS documents, architecture documents, and technical specifications
Using AI to summarize large technical documents
Extracting functional and non-functional requirements
Identifying cloud infrastructure, application, networking, security, and operational requirements
Using AI to identify dependencies between project requirements
Separating confirmed requirements from assumptions
Identifying missing information and requirement conflicts
Analyzing existing infrastructure documentation and configuration files
Creating project requirement summaries using AI
Building a requirements traceability matrix
Identifying technical risks and implementation constraints
Maintaining a clear record of decisions and unresolved questions
What you'll build
- Provide an AI assistant with a sample corporate cloud project's SRS and infrastructure requirements. Generate a structured project summary, identify missing information, and prepare a list of items that need confirmation before development begins.
By the end: Engineers will be able to understand project requirements faster, extract relevant technical information, and use AI to reduce manual document analysis while avoiding unsupported assumptions.
Teach engineers how to use AI to prepare the right questions for clients and stakeholders before designing or implementing a cloud solution.
Topics covered
Understanding the client's business problem
Identifying users, stakeholders, and system boundaries
Clarifying functional requirements and acceptance criteria
Understanding expected traffic, data volume, and performance requirements
Identifying availability, scalability, and disaster recovery expectations
Clarifying compliance, data residency, and security requirements
Understanding existing applications and infrastructure
Identifying third-party integrations and dependencies
Clarifying budget, timeline, and delivery constraints
Using AI to generate stakeholder interview questions
Using AI to identify ambiguous requirements
Generating follow-up questions based on client responses
Prioritizing questions by technical impact and delivery risk
Converting client answers into structured requirements
Documenting assumptions, decisions, and outstanding questions
What you'll build
- Use a sample requirement such as migrating an existing business application to AWS. Ask AI to generate client discovery questions covering architecture, traffic, data, security, availability, budget, and migration constraints. Refine the questions into a client questionnaire.
By the end: Engineers will be able to prepare structured client discovery sessions, identify critical information early, and reduce development delays caused by unclear or incomplete requirements.
Enable engineers to transform existing SRS documents and client requirements into clear, actionable Product Requirements Documents (PRDs) and implementation specifications.
Topics covered
Understanding the relationship between SRS, PRD, technical specifications, and implementation plans
Extracting requirements from lengthy SRS documents
Using AI to organize requirements into features and capabilities
Identifying user roles and user journeys
Writing problem statements and project objectives
Defining functional and non-functional requirements
Converting requirements into epics, features, and user stories
Creating acceptance criteria for individual features
Identifying dependencies and constraints
Mapping requirements to cloud components
Generating initial API and integration requirements
Identifying security and compliance requirements
Creating requirement-to-test traceability
Reviewing generated documentation for omissions and contradictions
Maintaining links to original source requirements
What you'll build
- Provide an SRS for a cloud-hosted employee management application. Use AI to generate a structured PRD containing project objectives, features, user stories, acceptance criteria, dependencies, and non-functional requirements. Compare the generated PRD with the original SRS and correct any unsupported assumptions.
By the end: Engineers will be able to convert source requirements into actionable project documentation faster while preserving traceability to the original requirements.
Teach engineers to create reusable master prompts that guide AI assistants through corporate cloud engineering tasks with consistent project context, constraints, and output expectations.
Topics covered
Why generic prompts often produce incomplete engineering outputs
Designing a master prompt for a corporate cloud project
Defining the AI's role and technical responsibilities
Supplying project objectives and business context
Providing the technology stack and existing architecture
Defining coding conventions and infrastructure standards
Specifying required output formats and deliverables
Defining security, compliance, and cost constraints
Instructing AI to distinguish facts, assumptions, and recommendations
Asking AI to inspect existing code before proposing changes
Requiring a plan before multi-file implementation
Defining acceptance criteria and verification steps
Using task-specific prompts alongside the master prompt
Refining prompts based on incorrect or incomplete results
Maintaining reusable prompts in version control
What you'll build
- Create a reusable master prompt for an AWS application project. Require the AI assistant to inspect the repository, summarize the architecture, identify relevant requirements, propose a plan, implement approved changes, run available tests, and report unresolved issues.
By the end: Defining security, compliance, and cost constraints Instructing AI to distinguish facts, assumptions, and recommendations Asking AI to inspect existing code before proposing changes Requiring a plan before multi-file implementation Defining acceptance criteria and verification steps Using task-specific prompts alongside the master prompt Refining prompts based on incorrect or incomplete results Maintaining reusable prompts in version control
Teach engineers to provide AI assistants with the correct project information so that generated code, infrastructure configurations, and documentation remain consistent with the existing system.
Topics covered
Understanding project context engineering
Distinguishing persistent instructions from task-specific context
Building a project context pack
Providing repository structure and architecture documentation
Maintaining project conventions and technology decisions
Creating an architecture decision record
Recording database schemas and API contracts
Maintaining environment and deployment documentation
Using AI to summarize large codebases
Providing relevant files instead of excessive unrelated context
Using retrieval and targeted file inspection to find information
Maintaining context across multiple development sessions
Updating project instructions when architecture changes
Avoiding outdated documentation and conflicting instructions
Separating approved requirements from AI-generated suggestions
What you'll build
- Create a project context directory for a sample AWS application containing a project overview, architecture diagram, technology stack, coding standards, environment information, and acceptance criteria. Use the context to ask AI to implement a small feature consistent with the existing project.
By the end: Engineers will be able to create reliable project context for AI-assisted development, reduce repeated explanations, and minimize inconsistent outputs caused by missing or outdated information.
Enable engineers to use AI to understand unfamiliar corporate repositories, trace application behavior, and identify where a change should be implemented before modifying the code.
Topics covered
Understanding unfamiliar enterprise repositories
Generating repository summaries and architecture overviews
Identifying application entry points and execution flows
Understanding module relationships and dependencies
Tracing API requests through application components
Identifying database access and integration points
Understanding environment configuration
Analyzing existing Dockerfiles and deployment manifests
Explaining Terraform and CloudFormation files
Identifying duplicated logic and technical debt
Finding relevant files using targeted searches
Generating dependency and component summaries
Identifying the potential impact of a requested change
Asking AI to explain code with references to actual files
Validating explanations against the source code
What you'll build
- Provide an unfamiliar cloud application's repository to an AI coding assistant. Ask it to explain the application flow, identify key configuration files, trace a sample request, and list the files likely to require changes for a new feature.
By the end: Engineers will be able to navigate unfamiliar codebases faster and make better-informed implementation decisions without manually reading every file.
Teach engineers to use AI to compare architectural options and select cloud services based on business requirements, constraints, and operational needs.
Topics covered
Translating business requirements into architecture decisions
Evaluating compute, storage, database, and networking options
Choosing between virtual machines, containers, and serverless services
Choosing between relational and NoSQL databases
Evaluating synchronous and asynchronous communication
Comparing monolithic, microservices, and event-driven architectures
Designing for availability, scalability, and fault tolerance
Evaluating single-region and multi-region designs
Using AI to generate alternative architecture proposals
Comparing trade-offs across cost, complexity, performance, and reliability
Identifying service dependencies and operational requirements
Using AI to draft architecture decision records
Validating recommendations against official cloud documentation
Avoiding unnecessary architectural complexity
Requiring human approval for significant architectural decisions
What you'll build
- Ask AI to compare two architecture options for a corporate order-processing application. Evaluate the proposed services, cost implications, availability, operational complexity, and security controls before documenting the selected architecture.
By the end: Engineers will be able to use AI to accelerate architecture evaluation and produce better-documented technical decisions while retaining responsibility for the final design.
Teach engineers to evaluate when AI is useful in a cloud project and select appropriate AI models, frameworks, and tools according to the actual engineering problem.
Topics covered
Identifying tasks that benefit from AI assistance
Distinguishing coding assistants, general-purpose LLMs, and specialized models
Selecting a model for code generation and repository analysis
Selecting a model for document analysis and requirement extraction
Selecting a model for log analysis and troubleshooting
Selecting a model for test generation and code review
Understanding model context-window requirements
Comparing output quality, latency, and cost
Understanding hosted models versus self-hosted models
Evaluating data privacy and enterprise access requirements
Choosing between direct model APIs and orchestration frameworks
Understanding when frameworks such as LangChain or LangGraph are useful
Evaluating retrieval frameworks for document-grounded applications
Understanding when a specialized AI application is justified
Testing model performance against representative project tasks
Avoiding tool selection based solely on popularity
Documenting model and framework selection criteria
What you'll build
- Evaluate two available AI models for analyzing an SRS and generating cloud infrastructure code. Compare correctness, completeness, response time, cost, and security requirements using the same test cases.
By the end: Engineers will be able to select AI models and frameworks based on measurable project needs rather than adopting unnecessary tools or assuming that one model is best for every task.
Teach engineers to use AI to accelerate the creation, explanation, refactoring, and validation of infrastructure code for corporate cloud environments.
Topics covered
Infrastructure as Code principles
Terraform and AWS CloudFormation fundamentals
Generating initial infrastructure templates from requirements
Creating VPCs, subnets, security groups, and compute resources
Defining IAM roles and permissions
Creating storage and database resources
Reusing infrastructure modules
Managing environment-specific configurations
Understanding dependencies and resource ordering
Using AI to explain existing infrastructure code
Detecting configuration errors and missing parameters
Generating validation commands and test cases
Reviewing infrastructure execution plans
Identifying infrastructure drift
Separating development, staging, and production configurations
Protecting secrets and sensitive configuration values
Reviewing cost and security implications before deployment
What you'll build
- Generate an initial Terraform configuration for a development environment using AI. Validate the configuration, inspect the plan, review permissions and networking, and apply changes only to an approved test environment.
By the end: Engineers will be able to accelerate infrastructure coding and review while ensuring that generated configurations are validated before cloud resources are created or modified.
Enable developers to use AI coding assistants to implement cloud application features more quickly while following existing architecture and engineering standards.
Topics covered
Translating user stories into implementation tasks
Breaking large features into manageable development steps
Generating code aligned with the existing repository
Creating API endpoints and service logic
Integrating applications with AWS services
Generating database access and validation logic
Reusing existing functions and components
Asking AI to explain unfamiliar code before editing it
Using AI autocomplete for repetitive coding
Refactoring code without changing intended behavior
Implementing error handling and logging
Generating configuration templates
Reviewing multi-file changes and dependencies
Preventing unnecessary rewrites of working components
Comparing code changes against acceptance criteria
Using Git diffs to inspect AI-generated modifications
What you'll build
- Implement a feature that allows a cloud-hosted application to upload a document to Amazon S3 and store its metadata in a database. Use AI to assist with planning and implementation, then verify permissions, error handling, and integration behavior.
By the end: Developers will be able to use AI to accelerate routine implementation work while preserving project conventions, application behavior, and code maintainability.
Teach developers to use AI to build, document, test, and troubleshoot APIs that connect cloud applications and external enterprise systems.
Topics covered
Understanding REST API design
API endpoints, HTTP methods, and status codes
API specifications and OpenAPI documentation
Designing request and response schemas
Input validation and error handling
Authentication and authorization
Integrating API Gateway with AWS Lambda
Connecting applications to databases and cloud services
Integrating third-party enterprise APIs
Handling timeouts, retries, and rate limits
AI-assisted API specification generation
AI-assisted client and server code generation
Generating API tests from acceptance criteria
Explaining API errors and integration failures
Mocking external services during testing
Reviewing API changes for backward compatibility
What you'll build
- Generate an OpenAPI specification and an initial API implementation for a corporate employee service. Use AI to produce test cases, then verify endpoint behavior, validation, error responses, and integration with a sample database.
By the end: Engineers will be able to accelerate API development and integration work using AI while maintaining consistent contracts, reliable error handling, and testable behavior.
Teach engineers to use AI to investigate cloud failures, analyze error messages, correlate logs, and identify probable root causes more efficiently.
Topics covered
Common cloud application failure patterns
Understanding stack traces and exception messages
Debugging application and configuration errors
Analyzing AWS CloudWatch logs and metrics
Investigating EC2, Lambda, container, and API failures
Identifying permission and authentication errors
Diagnosing networking and DNS problems
Investigating database connectivity and query failures
Correlating events across distributed services
Providing AI with relevant logs and configuration excerpts
Asking AI to rank possible causes by supporting evidence
Distinguishing symptoms from root causes
Generating diagnostic commands and investigation steps
Validating proposed fixes in a safe environment
Recording root-cause analysis and corrective actions
Avoiding disclosure of credentials or confidential data in prompts
What you'll build
- Investigate a simulated AWS application outage involving a failed API request. Supply relevant logs and configuration snippets to AI, obtain a list of likely causes, and verify the actual cause through cloud monitoring and configuration checks.
By the end: Engineers will be able to use AI to shorten investigation cycles, prioritize diagnostic steps, and document evidence-based root causes instead of applying speculative fixes.
Enable developers and QA engineers to use AI to generate, organize, and improve test cases for cloud applications and infrastructure.
Topics covered
Understanding the role of testing in cloud projects
Translating requirements into test scenarios
Generating test cases from user stories and acceptance criteria
Positive, negative, boundary, and edge-case testing
Unit testing and integration testing
API testing
Database validation
Testing AWS Lambda functions
Testing event-driven workflows
Testing authentication and authorization behavior
Generating test data and mocks
Identifying missing test scenarios
Using AI to explain test failures
Prioritizing test cases by business risk
Maintaining traceability between requirements and tests
Reviewing generated tests for meaningful assertions
What you'll build
- Provide a feature specification for a cloud-hosted file upload service. Use AI to generate positive, negative, boundary, permission, and failure-recovery test cases. Implement selected tests and verify that they detect expected failures.
By the end: Engineers will be able to reduce manual test-design effort, improve scenario coverage, and identify defects earlier through AI-assisted testing.
Teach engineers to use AI to help implement automated tests, diagnose failing tests, and maintain test suites as cloud applications evolve.
Topics covered
Automating repeatable testing workflows
Pytest or an equivalent testing framework
Unit and integration test automation
Mocking cloud services and external dependencies
Using tools such as Moto or LocalStack where appropriate
API automation and contract testing
Browser-based testing of cloud application interfaces
Generating test fixtures and test data
Using AI to explain assertion failures
Distinguishing genuine defects from unstable tests
Detecting flaky tests and timing issues
AI-assisted repair of outdated test code
Reviewing whether generated fixes preserve intended behavior
Integrating automated tests into CI pipelines
Capturing test results and failure reports
Using coverage reports to identify untested paths
What you'll build
- Take a Python application with an incomplete test suite. Use AI to add missing unit tests, diagnose a failing integration test, and propose a repair. Run the suite and verify that the fix addresses the underlying problem.
By the end: Engineers will be able to accelerate test implementation and failure diagnosis while maintaining meaningful assertions and avoiding superficial fixes that merely make tests pass.
Teach developers to use AI to review code changes, identify maintainability problems, and improve code quality before merging changes into a corporate repository.
Topics covered
Code review objectives and review standards
Understanding Git diffs and pull requests
Detecting logic errors and edge cases
Identifying duplicated code and unnecessary complexity
Reviewing naming, structure, and readability
Detecting resource-management problems
Reviewing asynchronous and concurrent code
Identifying error-handling gaps
Reviewing cloud SDK usage and API calls
AI-assisted refactoring proposals
Generating code explanations for reviewers
Detecting possible performance bottlenecks
Checking consistency with project conventions
Separating high-impact defects from style suggestions
Running linters, formatters, and static analysis
Reviewing AI-generated changes before merging
What you'll build
- Use AI to review a pull request containing changes to a cloud application's API and database access logic. Classify the findings by severity, validate the important findings, and prepare a concise review report.
By the end: Developers will be able to improve review speed and consistency, detect common defects earlier, and reduce repetitive review work without replacing human code ownership.
Enable cloud engineers to use AI to support security reviews, policy analysis, compliance evidence preparation, and identification of risky cloud configurations.
Topics covered
AWS shared responsibility model
Least-privilege access and IAM policy review
Encryption at rest and in transit
Secret and credential management
Network exposure and security group review
Secure storage and database configurations
CloudTrail and audit-log analysis
AWS Security Hub and GuardDuty concepts
Vulnerability scanning and dependency analysis
Compliance requirements and evidence collection
AI-assisted explanation of security findings
AI-assisted threat modeling
Reviewing Terraform and CloudFormation security
Identifying insecure defaults and unnecessary permissions
Data privacy and secure use of AI coding assistants
Validating recommendations against official security guidance
Human approval for access, policy, and production security changes
What you'll build
- Review a sample cloud architecture and its infrastructure code with AI. Identify potentially public resources, excessive permissions, missing encryption, and exposed secrets. Validate the findings and produce a remediation plan.
By the end: Engineers will be able to use AI to accelerate security reviews and risk documentation while retaining human responsibility for security decisions and compliance.
Teach engineers to use AI to create and troubleshoot continuous integration and deployment workflows for corporate cloud applications.
Topics covered
Continuous Integration and Continuous Delivery/Deployment
Git branching and pull request workflows
Build, test, package, and deployment stages
GitHub Actions and AWS CI/CD services
AWS CodeBuild, CodeDeploy, and CodePipeline concepts
Infrastructure deployment pipelines
Environment-specific configuration
Automated quality gates
Artifact management and versioning
Deployment approvals and release controls
AI-assisted workflow file generation
AI-assisted build-log interpretation
Diagnosing dependency and compilation failures
Generating pipeline test steps
Dependency and supply-chain security checks
Rollback strategies and failed deployment recovery
Preventing unauthorized production changes
What you'll build
- Create a CI pipeline for a cloud-hosted Python application. Use AI to generate the initial workflow, configure automated tests, simulate a build failure, and use the logs to identify and correct the issue.
By the end: Engineers will be able to accelerate CI/CD setup and troubleshooting while preserving automated validation, deployment approvals, and safe release practices.
Enable engineers to use AI to accelerate containerization, orchestration configuration, deployment troubleshooting, and operational documentation.
Topics covered
Dockerfiles and container image construction
Image layers and build optimization
Docker Compose
Container registries and Amazon ECR
Kubernetes Deployments, Services, and Pods
ConfigMaps, Secrets, and resource limits
Amazon ECS, Fargate, and EKS concepts
Container networking and storage
Readiness and liveness probes
Autoscaling and resource requests
AI-assisted Dockerfile generation and review
AI-assisted Kubernetes manifest generation
Debugging image build failures
Troubleshooting CrashLoopBackOff and scheduling failures
Reviewing resource limits and health checks
Container vulnerability scanning
Validating deployment changes in test environments
What you'll build
- Containerize a small application and deploy it to a test Kubernetes environment or approved local cluster. Use AI to assist with configuration and diagnose a deliberately introduced readiness-probe or environment-variable error.
By the end: Engineers will be able to use AI to accelerate container development and troubleshooting while validating configuration, security, and runtime behavior.
Teach engineers and operations teams to use AI to analyze monitoring data, summarize incidents, and improve cloud service reliability.
Topics covered
Cloud monitoring and observability fundamentals
Metrics, logs, traces, and events
Amazon CloudWatch dashboards and alarms
AWS CloudTrail and configuration history
Distributed tracing concepts
Service-level indicators and objectives
Application and infrastructure health checks
Alert correlation and alert noise
Incident timelines and evidence collection
AI-assisted log summarization
AI-assisted detection of unusual patterns
Generating incident summaries and post-incident reports
Root-cause hypothesis generation
Verifying AI conclusions against telemetry
Runbooks and operational checklists
Human approval for operational remediation
Measuring incident response and recovery time
What you'll build
- Analyze a simulated service degradation using logs, metrics, and a deployment timeline. Use AI to prepare a ranked list of likely causes and a draft incident report, then verify the findings against the monitoring evidence.
By the end: Engineers will be able to use AI to speed up incident analysis, improve operational reporting, and make troubleshooting more systematic and evidence-based.
Teach cloud engineers to use AI to identify potential cost-saving opportunities and compare optimization options without compromising reliability or business requirements.
Topics covered
Cloud pricing models and billing concepts
AWS Cost Explorer and AWS Budgets
Resource tagging and cost allocation
EC2 instance sizing and utilization
Storage class selection and lifecycle policies
Database sizing and scaling
Autoscaling and workload scheduling
Data transfer and network-related costs
Identifying idle and underutilized resources
AI-assisted cost report summarization
Generating cost optimization recommendations
Comparing architecture costs and trade-offs
Identifying potential savings from workload changes
Forecasting and budget variance analysis
Understanding performance and reliability trade-offs
Validating savings estimates with pricing tools
Approval controls for resource termination or resizing
What you'll build
- Analyze a sample cloud cost report. Use AI to identify high-cost resources, prepare optimization recommendations, and compare potential savings with performance, availability, and operational risks.
By the end: Engineers will be able to accelerate cost analysis and generate evidence-based optimization proposals while validating actual pricing and preserving required service levels.
Teach engineers to use AI to analyze existing applications, plan cloud migration, identify modernization opportunities, and prepare migration documentation.
Topics covered
Cloud migration strategies and the 6 Rs
Existing infrastructure and application discovery
Application dependency mapping
Data migration considerations
Network and identity dependencies
Compatibility and operational constraints
AI-assisted analysis of legacy architecture documents
Generating migration assessment reports
Comparing rehosting, replatforming, and refactoring
Identifying migration risks and assumptions
Planning migration waves
Creating migration checklists and runbooks
Estimating effort and dependencies
Testing migrated applications
Rollback and recovery planning
Validating data integrity and application behavior
Documenting post-migration improvements
What you'll build
- Analyze a sample legacy application and use AI to prepare a migration assessment, identify dependencies, compare migration approaches, and produce a phased migration plan with testing and rollback steps.
By the end: Engineers will be able to accelerate migration assessment and planning while ensuring that recommendations are checked against the real application's dependencies and business constraints.
Enable engineers to use AI to generate and maintain technical documentation, operational procedures, architecture records, and handover materials throughout the project lifecycle.
Topics covered
Technical documentation standards
Architecture and infrastructure documentation
API documentation and interface contracts
README and repository setup documentation
Environment configuration guides
Deployment and rollback procedures
Operational runbooks
Incident response procedures
Requirements and test traceability
AI-assisted documentation generation from source code
Generating diagrams and architecture descriptions
Converting technical notes into structured documents
Keeping documentation aligned with code changes
Creating troubleshooting knowledge bases
Preparing developer onboarding guides
Preparing client handover documentation
Reviewing generated documentation for accuracy and confidentiality
What you'll build
- Take a completed cloud application repository and use AI to produce a README, architecture overview, environment setup guide, deployment runbook, and troubleshooting document. Verify each document against the actual implementation.
By the end: Engineers will be able to reduce repetitive documentation effort and maintain more consistent technical knowledge across development, operations, and client handover.
Teach engineers to structure AI-assisted cloud development workflows using agentic coding environments, persistent project instructions, reusable skills, and controlled multi-step execution.
Topics covered
Understanding agentic AI development environments
Using Google Antigravity to work with project files and development tasks
Using Claude and Claude Code for codebase analysis and implementation
Understanding the difference between a prompt, project instructions, a skill, and an external tool integration
Creating a project-specific master prompt
Creating persistent project context and engineering rules
Defining reusable skills for recurring cloud engineering tasks
Writing clear skill descriptions and task instructions
Building skills for:
SRS-to-PRD conversion
Cloud architecture review
Terraform generation and validation
IAM policy review
API implementation
Test generation
Code review
Log analysis
CI/CD troubleshooting
Deployment readiness review
Understanding skill discovery and invocation
Using skills to standardize repeatable workflows
Using separate agents or tasks for independent reviews where supported
Reviewing agent-generated plans, code changes, and reports
Understanding tool permissions and execution boundaries
Avoiding uncontrolled production changes
Measuring the impact of reusable skills on delivery speed and quality
What you'll build
- Create a project-specific cloud engineering skill that guides an AI assistant through a Terraform change. The skill should require requirement inspection, a proposed implementation plan, configuration generation, validation, security review, and a final change report.
By the end: Engineers will understand how to create reusable AI-assisted engineering workflows, reduce repeated prompting, and standardize common cloud project tasks across a team.
Teach engineers to use AI to consolidate testing evidence, review release readiness, identify unresolved risks, and prepare a cloud application for controlled production deployment.
Topics covered
Mapping requirements to implementation and tests
Reviewing acceptance criteria and traceability
Consolidating unit, integration, API, and system test results
Generating missing test scenarios
Reviewing performance and load-test evidence
Security and vulnerability review
Infrastructure validation and configuration checks
Reviewing monitoring and alerting coverage
Backup and recovery verification
Deployment and rollback readiness
AI-assisted release checklist generation
Identifying unresolved defects and known limitations
Summarizing test evidence for stakeholders
Reviewing dependency and environment differences
Producing a deployment risk assessment
Generating release notes and change summaries
Human sign-off and approval gates
Post-deployment smoke testing and verification
What you'll build
- Use a sample cloud application release candidate. Supply test reports, infrastructure plans, security findings, and deployment notes to an AI assistant. Generate a release readiness report that identifies passed checks, failed checks, unresolved risks, and required approvals.
By the end: Engineers will be able to use AI to consolidate release evidence and identify readiness gaps while ensuring that production deployment decisions remain grounded in verified results and authorized approvals.
Apply the complete AI-assisted engineering workflow to a realistic corporate cloud project, demonstrating how AI can accelerate the journey from client requirements to a tested and documented cloud solution.
Topics covered
Understanding a client problem statement and existing SRS
Preparing client discovery questions
Generating and reviewing the PRD
Building the master prompt and project context
Establishing project instructions and reusable skills
Analyzing the existing repository or starting architecture
Selecting appropriate cloud services and AI-assisted tools
Designing and documenting the cloud architecture
Generating infrastructure code using AI
Implementing application features with AI assistance
Developing APIs and service integrations
Creating automated test cases
Running tests and diagnosing failures
Reviewing code, infrastructure, and security changes
Building a CI/CD pipeline
Preparing monitoring and operational procedures
Evaluating cost and reliability trade-offs
Validating deployment and rollback procedures
Preparing technical documentation and handover materials
Measuring delivery efficiency and output quality
What you'll build
- Complete one corporate-style cloud project using a controlled AI-assisted workflow. A suitable example is a cloud-hosted employee or inventory management application with:
- A documented client problem statement and requirements
- A reviewed PRD and acceptance criteria
- A documented AWS architecture
- Infrastructure defined using Terraform or CloudFormation
- An application API and database integration
- Automated tests and a CI pipeline
- Logging, monitoring, and security controls
- A validated deployment to an approved test environment
- Technical documentation and a final demonstration
- Use Google Antigravity, Claude, or another approved AI assistant where appropriate. Record which tasks were accelerated by AI, what required human correction, and how the final result was verified.
By the end: Engineers will demonstrate an end-to-end AI-assisted cloud delivery workflow. They will be able to explain how AI accelerated requirements analysis, implementation, testing, troubleshooting, documentation, and release preparation while retaining responsibility for correctness, security, and operational safety.
Live client projects — 5+ across the program
Across the Career-Ready program you work on all real client projects, contributing to each at a level appropriate to your current skills. By the final project, you're working at near-professional capacity.
Live client project
Hash-Based Auto-Deploy System
Git commit hash tracking, automated container build triggers, artifact validation, and immutable multi-environment deployment.
AWS EC2S3DockerGitHub ActionsBash
Live client project
Cloud Deployment Approval & Rollback Platform (CDARP)
Multi-stage release approval gates, automated smoke testing, instant one-click canary rollback, and deployment audit logging.
AWS LambdaAPI GatewayDynamoDBCloudWatchPython
Live client project
Internal Developer Platform (IDP)
Self-service cloud environment provisioning, developer access governance, resource templates, and infrastructure cost metrics.
AWS VPCTerraformECSPythonDocker
Live client project
Cloud Sandbox Management Platform
Ephemeral test environment provisioning, automatic idle-resource destruction, budget capping, and security policy enforcement.
AWS CloudFormationLambdaIAMEventBridgePython
Live client project
Production Readiness Assessment Platform
Automated AWS Well-Architected framework auditing, security group vulnerability scanning, and reliability scorecard generation.
AWS ConfigIAMSecurity HubPythonBoto3
Live client project
Intelligent Cloud Troubleshooting Assistant
Log aggregation, CloudWatch alarm correlation, automated error triage, and root-cause remediation runbook recommendations.
AWS CloudWatchOpenSearchLambdaPythonSlack API
Everything included in your enrollment
✓
400+ live training hours — 5 hours daily, every weekday✓
100+ Immersive VR sessions + Unlimited VR revision for up to 2 full years (exclusive flagship privilege)✓
All 4 programs: internship + career course + industry projects + AI development✓
AI tools configured on your laptop (ChatGPT-4, Gemini, Copilot, Cursor — value ₹15,000+)✓
Internship + course certificate from WhiteandBox.com✓
Offer letter — verified, 0 background check issues✓
Letter of Recommendation (performance-based)✓
LinkedIn profile building, portfolio guidance, and soft skills training✓
Digital certificate with QR code verification✓
Free access to client summits and webinars (with certificate)✓
1:1 daily mentorship from senior developers✓
Remote, hybrid, or in-office at BangaloreCareer-Ready Cloud Computing & AWS Flagship Program
400+ live hours · 4 complete programs · 70+ modules · Full placement support. The program serious developers choose when they want job outcomes, not just a certificate.
₹38,000
₹17,850
50% off · limited seats
You save ₹20,150 at current offer — includes AI tools worth ₹15,000+ configured free on your laptop