jcorpac 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.