Model Monitoring & Drift Detection

Summary

Tracking a deployed model's live performance and input distribution over time — catches silent failures like data drift that unit tests can never detect.

How to Learn This

1Log prediction inputs/outputs from a deployed model for later analysis.
2Learn to detect data drift by comparing live input distributions to training data.
3Set up an alert for when live performance metrics drop below a threshold.

CI/CD for ML Pipelines

A/B Testing Models

More topics in MLOps & Model Deployment

Model Serving (FastAPI/Flask)Docker for ML Models Model Versioning Experiment Tracking (MLflow, Weights & Biases)CI/CD for ML Pipelines A/B Testing Models Batch vs Real-time Inference Kubernetes for ML (Overview)Feature Stores (Overview)

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