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. https://aws.amazon.com/free/
- 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)