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Click on the Project for the details and description, submit the project documents for each project using the Project Submission Form

Foundation Level (Beginner-Friendly)

1. Serverless Web Application
Build a complete serverless web application using AWS Lambda, API Gateway, DynamoDB, and S3 for static hosting.

Key Components:

  • Single-page application frontend hosted on S3

  • RESTful API using API Gateway and Lambda

  • User authentication with AWS Cognito

  • Data storage with DynamoDB

Skills Gained: Serverless architecture, API development, NoSQL databases, cloud hosting

2. Automated Backup Solution
Create an automated backup system for EC2 instances and RDS databases using AWS Lambda, CloudWatch Events, and S3.

Key Features:

  • Scheduled automated backups

  • Cross-region replication

  • Lifecycle policies for cost optimization

  • Disaster recovery procedures

Skills Gained: Backup strategies, automation, disaster recovery, cost management

Medium Level (Intermediate)

3. Real-Time Data Pipeline
Build a comprehensive data pipeline that ingests, processes, and analyzes streaming data using AWS Kinesis, Lambda, Glue, and QuickSight.

Key Components:

  • Real-time data ingestion with Kinesis Data Streams

  • Stream processing with Kinesis Analytics

  • Data transformation using AWS Glue

  • Visualization dashboards with QuickSight

Skills Gained: Stream processing, ETL pipelines, data analytics, real-time visualization

4. Microservices Architecture on ECS
Deploy a microservices-based application using Amazon ECS, Application Load Balancer, and RDS.

Key Features:

  • Container orchestration with ECS Fargate

  • Service discovery and load balancing

  • Auto-scaling based on metrics

  • Blue-green deployment strategies

Skills Gained: Microservices architecture, container orchestration, load balancing, deployment strategies

Expert Level (Advanced)

5. AI/ML Model Deployment Platform
Create a comprehensive machine learning platform using SageMaker, Lambda, and API Gateway for model training, deployment, and inference.

Key Components:

  • Automated model training pipelines

  • A/B testing for model versions

  • Real-time inference endpoints

  • Model monitoring and retraining

Skills Gained: MLOps, model deployment, inference optimization, automated retraining

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