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Building AI Data Pipelines for Model Quality

7 chapters · 33 lessons

1. 1. Task Design and Data Requirements

   4 lessons

   Translate business problems into structured data specifications that guide collection and labeling.

2. 2. Data Collection and Generation Strategies

   5 lessons

   Build systems to acquire, generate, and augment data that represents real operating conditions.

3. 3. Quality Control and Review Workflows

   5 lessons

   Implement automated and human review systems that catch errors before they degrade models.

4. 4. Data Lineage and Versioning Systems

   4 lessons

   Track data provenance and changes to enable reproducible training and debugging.

5. 5. Building Evaluation and Test Sets

   5 lessons

   Create measurement datasets that reveal model weaknesses and guide improvement.

6. 6. Data Infrastructure for Retrieval Systems

   5 lessons

   Build pipelines that prepare documents for retrieval augmented generation and semantic search.

7. 7. Production Data Operations and Iteration

   5 lessons

   Operate data pipelines that respond to model failures and evolving requirements.