Choose Your Path

Three paths, each built for a different stage of your career. Pick the one that fits and start building.

FRESHER

AI Engineering Fundamentals

You're newer to engineering and want to get into AI/ML. You don't need ML math — you need to build and deploy real things.

8 projects · Starts from Python

Outcome: junior AI engineer–ready

  1. 1. Chatbot with AWS Bedrock
  2. 2. Vector search with OpenSearch Serverless
  3. 3. Your first RAG pipeline
  4. 4. Serverless inference on Lambda
  5. 5. Prompt engineering & evaluation
  6. 6. Fine-tuning with Amazon Titan
  7. 7. Multi-modal app with Bedrock Vision
  8. 8. Capstone: full-stack AI assistant
Start Path 1
MID-CAREER

AI-Augmented Engineering

You have 2–8 years of software experience. AI is everywhere and you feel behind. These projects put AI into the work you already know how to do.

8 projects · Any language welcome

Outcome: AI in your toolkit

  1. 1. AI-powered code review bot
  2. 2. Semantic search for your codebase
  3. 3. LLM-based test generation
  4. 4. Automated incident summariser
  5. 5. Copilot for internal docs
  6. 6. AI observability with tracing
  7. 7. Agent that writes & runs SQL
  8. 8. Capstone: AI-augmented dev workflow
Start Path 2
ML-FOCUSED

ML Engineering on AWS

You've done some ML or data science and want to own the full system — training, serving, monitoring — in production on AWS.

8 projects · Python + ML exposure

Outcome: own ML systems end-to-end

  1. 1. Custom training job on SageMaker
  2. 2. Experiment tracking with MLflow
  3. 3. Model serving with SageMaker Endpoints
  4. 4. Feature store with SageMaker Feature Store
  5. 5. A/B testing inference endpoints
  6. 6. Model monitoring & drift detection
  7. 7. Retraining pipeline with Step Functions
  8. 8. Capstone: end-to-end MLOps platform
Start Path 3

None of these feel right?

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