ml-ops

Deploy ML models to production using monitored canary rollouts and shadow comparisons to validate changes statistically before full promotion, ensuring production-safe rollback strategies. This approach prevents silent quality regressions during model deployment.

Experiment tracking standardizes how hyperparameters, metrics, and artifacts are logged so machine learning results can be replayed and audited. This ensures reproducibility across training pipelines and satisfies experiment traceability requirements.

Drift monitoring detects data and prediction drift in production ML models by triggering drift-aware alerting. It identifies when feature distributions change over time, enabling teams to catch silent quality regressions before they impact business outcomes.

A/B testing and canary deployments validate ML model changes with statistical rigor tied to business outcomes. You can run canary or shadow comparisons to compare new models against existing ones before executing production-safe promotion.

Feature store integration is needed for model deployment when requiring point-in-time correctness to prevent training-serving skew. It ensures consistent feature computation across training pipelines and production serving environments.