01Module 03: Data VersioningVersioning datasets as first-class artifacts - DVC, Delta Lake, dataset lineage, data contracts, and managing ML datasets at scale.02Why Data VersioningThe case for treating datasets as first-class versioned artifacts - regulatory requirements, reproducibility, drift detection, and the approaches to versioning (full copy, delta, pointer).03DVC: Data Version ControlDVC in production - pointer files, remote storage, pipeline definitions (dvc.yaml), caching, dvc repro, CI/CD integration, and versioning 500GB datasets without bloating git.04Delta Lake and Iceberg for MLDelta Lake as ML data infrastructure - ACID transactions, time travel, schema evolution, Delta + MLflow integration, OPTIMIZE/Z-ordering, and handling schema changes without breaking pipelines.05Dataset Lineage and ManagementTracking dataset provenance, preventing train/val/test leakage, stratified splitting, dataset registries, and discovering the CV team's 12% accuracy inflation from augmentation leakage.06Data ContractsEnforcing data quality agreements between producers and consumers - schema contracts with Pandera and Great Expectations, statistical contracts, SLA contracts, CI integration, and violation alerting.
