MLOps training & inference pipeline
End-to-end ML lifecycle: feature store, training pipeline, model registry, online inference, and monitoring.
The prompt
End-to-end MLOps pipeline. Training side: data scientists write training pipelines orchestrated by Kubeflow on a Kubernetes cluster. Pipelines pull features from a Feast feature store, train models on GPU nodes, log experiments to MLflow, and register accepted models in the MLflow Model Registry. CI runs on every PR: data validation, training reproducibility check, model performance gate. Inference side: an online inference service deployed via KServe serves real-time predictions, reading features from the online feature store (Redis). Batch inference jobs write predictions to a data warehouse. Show the data drift detection, model performance monitoring (alerts to PagerDuty on PSI > threshold), and the canary rollout for new model versions.
What it generates
A complete MLOps diagram with training, model registry, online and batch inference, and production monitoring.
When to use it
When you have ML in production and need a system that handles training reproducibility, model versioning, online serving, and monitoring as one coherent platform.
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