ML Feature Store Pipeline
About This Architecture
End-to-end ML feature store pipeline: raw data from event logs, databases, and streams flows through feature engineering into a central feature store (Feast/Tecton) with offline (historical) and online (low-latency) stores. Training pipeline performs batch model training and evaluation; serving pipeline provides real-time predictions via API endpoints. Model registry (MLflow) tracks versioned models.
Architecture prompt
End-to-end ML feature store pipeline: raw data from event logs, databases, and streams flows through feature engineering into a central feature store (Feast/Tecton) with offline (historical) and online (low-latency) stores. Training pipeline performs batch model training and evaluation; serving pipeline provides real-time predictions via API endpoints. Model registry (MLflow) tracks versioned models.
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