weights-and-biases

To track ML experiments, initialize a W&B run and start logging basic metrics to automatically capture experiment data, visualize results in real-time dashboards, and compare runs across projects without manual log gathering.

Orchestrating hyperparameter sweeps involves configuring parameter ranges to automatically run, compare, and evaluate multiple configurations across your ML projects, streamlining the search for optimal model performance.

Artifact management tracks artifact lineage and integrates with a model registry to provide governance, ensuring proper versioning and traceability of datasets and models throughout the ML lifecycle.

Yes, you can compare run metrics across different data science projects by logging experiments to centralized dashboards, which automatically aggregate visuals and metrics for cross-project run comparisons.

Integrating a model registry is required to properly manage artifact lineage and governance, automatically tracking dataset versions and model states to maintain traceability throughout the ML lifecycle.