Build & Learn: From Data Science to AI Engineering Week 1

📅 Week 1: Kickoff — From Data Science to AI Engineering
After a short break, Build & Learn is back with a new seven-week focus:
learning how to build and ship real AI applications. 👩‍💻

AI engineering is becoming an increasingly important path for data scientists, analysts and Python developers. The biggest shift is not simply learning more machine learning theory—it is learning how to turn an AI prototype into a working application that other people can use.

Over seven weeks, we will build AND deploy basic end to end LLM/AI project touching key technologies ( FastAPI, Postgres, Docker, a real AI pipeline. )

• Week 1 — Kickoff & Setup: Set up Python, GitHub, Docker, API keys, and run the starter project locally.
• Week 2 — Learn the LLM specific layer. (OpenAI API)
• Week 3 — Build production ready AI backends. (Fast API, MCP server)
• Week 4 — Connect AI to your data with RAG (vector database)
• Week 5 — Lean into evals and observability (tracing, evaluation)
• Week 6 — Build & Ship Your Version
• Week 7 — Demo Day

By the end of the 7 weeks you will have a working AI application in your own GitHub repository and experience building an AI pipeline, backend, database and frontend.

✨ Who is this for?
This cycle is designed for data scientists, analysts, software developers and anyone interested in transitioning toward AI engineering.
No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation before the first build session.

✨ Who’s hosting?
I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products.
I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and about moving beyond AI hype toward applications that actually work.

💻 Bring: Your laptop and curiosity. Please also grab ☕ at the octopus bar to support their business and providing the location!