MLflow Production Tracking

MLflow is an open-source platform for managing the complete machine learning model lifecycle. It has four main components: Tracking (logging metrics and parameters during training), Projects (packaging code and dependencies), Model Registry (versioning and promoting models), and Serving (deploying models as REST APIs or batch predictions). A typical workflow: you train 20 models with different hyperparameters, MLflow logs all metrics and artifacts (model files, plots, reports). You compare experiments side-by-side, pick the best performer, register it as "production," and deploy it. In production, you log predictions and ground-truth labels, detect model drift, and trigger retraining when performance degrades.