Model Training Pipelines¶

Coming soon.

• Orchestrating training as a pipeline (DAG of steps), not a one-off notebook run
• Reproducibility: pinning code, data, and config so a run can be repeated exactly
• Retraining triggers — scheduled, drift-driven, or manual
• Data, code, and model lineage: tracing a production model back to what produced it

Related¶

• Feature Store — the reproducible inputs a training pipeline reads from
• Experiment Tracking & Model Registry — where each pipeline run gets logged and versioned
• Model Monitoring & Drift — the signal that triggers a retraining run

Part of MLOps → AI Engineering.