How does MLflow tracking differ from the Model Registry?

The distinction between MLflow tracking and the Model Registry is primarily based on their intended use cases and the lifecycle stages they address within machine learning workflows. Tracking is designed to facilitate experimentation by capturing and storing information about various experiments, including metrics, parameters, artifacts, and other relevant data as models are trained and evaluated. This allows data scientists and engineers to analyze different approaches, compare results, and iterate on model development in an environment focused on experimentation.

On the other hand, the Model Registry serves a different purpose: it is specifically designed for production management of machine learning models. This involves tracking models that have been selected for deployment, ensuring they are versioned, and maintaining records of the associated metadata. The Model Registry streamlines the deployment process by ensuring that only vetted, high-quality models make their way into production, allowing teams to manage model lifecycle transitions from experimentation to production systematically.

By highlighting these key functional differences, it becomes clear that tracking is more about the iterative experimentation process, while the Model Registry is about managing and deploying models in a structured and secure manner for production use.