AI & AI Systems Engineering track

AI Systems Engineering: From Data Platform to Production AI

How to build AI systems that people can rely on: grounded in governed data, measured with real evaluations, protected by guardrails, and run like any other production pipeline.

For: data engineers, analytics engineers, architects Prerequisite: SQL and Python In progress: chapters are published as they are written

How the tutorial is built

  • Systems, not demos. Every chapter covers the engineering around the model: data, retrieval, tests, security, cost and operations.
  • On the same lab. Examples use the warehouse, semantic model and Airflow from the other tracks, so AI is added to a platform that already exists.
  • Measured. Changes are judged by evaluation results, not by how good one answer looks.

Chapters