01Module 02: Experiment TrackingSystematic tracking of ML experiments - hyperparameters, metrics, artifacts, and models - so your team can reproduce results, compare runs, and ship better models faster.02Why Experiment TrackingThe business and technical case for tracking every ML experiment - what to track, why it matters, and what happens when you don't.03MLflow Deep DiveProduction MLflow setup for teams - tracking server architecture, autologging, custom logging, model registry, nested runs for HPO, and scaling to 500+ experiments per week.04Weights & Biases Deep DiveW&B for production ML teams - run tracking, sweeps, artifact versioning, collaborative reports, alerts, and how it compares to MLflow.05Hyperparameter OptimizationSystematic HPO - grid search, random search, Bayesian optimization with Optuna, Hyperband/ASHA pruning, and multi-objective optimization for production ML.06Artifact Management & Experiment OrganizationManaging ML artifacts at scale - naming conventions, tagging, parent-child relationships, archival policies, and finding the model that became production from 2000 runs.07Comparing and Selecting ModelsSystematic model comparison and selection - metric design, statistical significance testing, champion-challenger frameworks, and making defensible production promotion decisions.
