Important Considerations:

  • Start Small: These are mini-projects! Focus on the core elements and iterate to extend functionality later.
  • AWS Free Tier: Many of these projects can be started within the AWS Free Tier to manage costs.
  • Experiment and Explore: The best way to learn is by doing. Dive in and don’t be afraid to explore different combinations of services.

Project 1: Serverless Image Resizer

  • Requirement: Automatically resize images for optimized website performance.
  • Summary: Users upload images to S3, triggering a Lambda function to resize and store them back in S3.
  • Tools/Services:
    • AWS S3
    • AWS Lambda
    • Image manipulation library (Pillow, etc., for Python-based Lambda)

Project 2: Static Website with CI/CD

  • Requirement: Deploy a website, ensuring updates are reflected seamlessly.
  • Summary: Create an automated pipeline to build and deploy a static website when code changes are made.
  • Tools/Services:
    • AWS S3 (for website hosting)
    • AWS CodeCommit (or similar source control)
    • AWS CodeBuild (for building website resources)
    • AWS CodeDeploy (for deployment to S3)

Project 3: Scalable Web Application

  • Requirement: Handle variable traffic to a web application efficiently.
  • Summary: An application frontend on EC2 instances behind a load balancer, instances scale in/out based on demand.
  • Tools/Services:
    • AWS EC2
    • AWS Auto Scaling Group
    • AWS Elastic Load Balancer (ELB)

Project 4: Chatbot with Natural Language Processing

  • Requirement: Conversational interface for customer queries or simple tasks.
  • Summary: Build a chatbot to understand basic customer requests and provide information or perform actions.
  • Tools/Services:
    • AWS Lex (for building the conversational interface)
    • AWS Lambda (for backend logic and integrations)

Project 5: Serverless Data Pipeline

  • Requirement: Extract, transform, and load (ETL) data for analysis or reporting.
  • Summary: Use serverless technologies to collect data from a source, process it, and store it in a data warehouse.
  • Tools/Services:
    • AWS S3
    • AWS Lambda
    • AWS Glue (for data transformations)
    • AWS Athena or Redshift (for data storage and querying)

Project 6: Recommendation Engine

  • Requirement: Provide personalized product/content suggestions to users.
  • Summary: Utilize historical data to build a recommendation system.
  • Tools/Services:
    • AWS S3 (data storage)
    • AWS SageMaker (build and train recommendation models)
    • AWS Lambda (deploy the model as an API for recommendations)

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