Data Engineering for AICourse

AI Engineering / 6 weeks

Data Engineering for AI

Teaches engineers and operators how to build the data foundation AI needs: pipelines, contracts, feature stores, retrieval indexes, lineage, and quality that hold up under load.

Learning path

Six modules with practical application.

  1. Identifying upstream sources, agreeing on contracts, and protecting AI consumers from silent upstream changes
  2. Designing pipelines that are idempotent, observable, retriable, and built for the failures they will actually meet
  3. Producing features once, serving them online and offline consistently, and avoiding train serve skew
  4. Building retrieval indexes that find the right passage fast and keep finding it as content grows and changes
  5. Setting data quality checks at the right gates and tracing lineage so issues are found before they reach the model
  6. Setting access controls, masking, and audit so AI systems use data lawfully and the team can prove it

Loading course material…