ML Experiment Tracking Pro

ML development is non-linear. Tracking every variant is essential for finding the "winning" model.

Core Concepts

• Runs: A single execution of a training script.
• Experiments: A collection of related runs (e.g., "Optimizing Learning Rate").
• Artifacts: Storing models, plots, and datasets.

Frameworks

• MLflow: Open source, platform-agnostic tracking server.
• Weights & Biases (W&B): Collaborative, visual platform for deep learning teams.

Best Practices

• Auto-Logging: Use mlflow.autolog() for effortless tracking in supported frameworks.
• Tagging: Use tags to mark "Production Ready" or "Baseline" models.
• Nesting: Use nested runs for hyperparameter tuning trials.