Thomas Piacentini
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Learning path

AI Data Systems

Original text · English915 min

Build reliable data, feature, embedding, retrieval, evaluation, and observability pipelines for AI systems.

  1. phases/00-setup-and-tooling/09-data-management — 45 min
  2. phases/02-ml-fundamentals/08-feature-engineering — 90 min
  3. phases/02-ml-fundamentals/13-ml-pipelines — 120 min
  4. phases/10-llms-from-scratch/03-data-pipelines — 90 min
  5. phases/11-llm-engineering/04-embeddings — 75 min
  6. phases/11-llm-engineering/05-context-engineering — 90 min
  7. phases/11-llm-engineering/06-rag — 90 min
  8. phases/11-llm-engineering/07-advanced-rag — 90 min
  9. phases/11-llm-engineering/10-evaluation — 45 min
  10. phases/11-llm-engineering/13-production-app — 120 min
  11. phases/17-infrastructure-and-production/13-llm-observability — 60 min
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Reading checkpoints are self-reported. Test results are recorded separately and apply to the executed cases.

AI Engineering from Scratch · 3f9715c · MIT

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