Feature stores, data versioning, and quality pipelines
What feature stores solve (training-serving skew), Feast (offline/online stores, feature services, materialization), Tecton, feature engineering best practices, and point-in-time correctness
DVC (tracking data, pipelines, experiments), LakeFS (git-like branching for data), data lineage, reproducibility, and dataset registries
Great Expectations (expectations, suites, checkpoints, data docs), schema validation, anomaly detection, drift monitoring, and data contracts