Overview
The gap between a model that performs well in a notebook and a model that reliably serves production predictions is where most ML projects die silently. Models degrade as input distributions shift (data drift), as relationships between inputs and outcomes change (concept drift), and as upstream data pipelines break. Without monitoring, degradation is invisible until business metrics decline — weeks or months too late.
The ML Model Deployment & Monitoring System defines the full production lifecycle: deployment patterns (shadow, canary, blue-green), monitoring of prediction distributions and ground-truth outcomes, drift detection with statistical tests, SLOs, and automated retraining triggers.
What you get: - Deployment pattern selection (shadow, canary, blue-green) - Prediction distribution monitoring (not just performance) - Data drift detection (PSI, KL divergence, KS test) - Concept drift detection (performance drop over time) - Ground-truth collection strategy (when labels arrive late) - Prediction latency and throughput SLOs - Retraining triggers (time-based, drift-based, performance-based) - Incident response runbook for model failures
Built for: ML engineers, data scientists moving models to production, and MLOps teams who need a rigorous framework — not just "deploy and hope".