e2e_ai_engineering_bootcamp

This repo is dedicated to including all the practical exercises done during the End-to-Eng AI Engineering Bootcamp (by Aurimas), Cohort 5. Each week will have its own branch.

Set up

• Clone the repo
• Install python (at least version 3.11 - https://realpython.com/installing-python/)
• Install docker (https://docs.docker.com/get-docker/)
• Install uv (https://docs.astral.sh/uv/getting-started/installation/)
• Run uv sync to install the dependencies and create the virtual environment under as .venv folder
• Create a .env file in the root folder with your own API keys and settings based on the .env.example file
• If you want to run the notebooks, go to the notebooks folder, choose the notebook you want to run. Then, select the kernel based on the virtual environment you created and feel free to run the cells as needed
• If you want to run the containerized application, run make run-docker-compose from the root folder to start the containers, which will start the Streamlit app, the API, Qdrant vector DB and Postgres
• If you want to run the evaluations, run make run-eval-retriever from the root folder to run the retriever evaluation

Week 1

• Understand the AI product lifecycle
• Tooling Overview
• What is RAG?
• Embedding models and vector DB
• Implement a RAG pipeline with observability
• RAG pipeline evaluation

Week 2

• Hybrid Vector Search
• Prompt Management with YAML, Jinja and LangSmith Prompt Registry

Week 3

• Query Rewriting
• Tool using and ReAct Agent in LangGraph
• Routing Pattern

Week 4

• State persistence
• Human-in-the-loop
• MCP Server and MCP tools
• State Streaming

Week 5

• BREAK

Week 6

• Multi-agents architecture
• Database management as tools

Week7

• Coordinator Agent Evals
• A2A with LangGraph and MAS

Contact me:

• LinkedIn