Understanding the need for the Model Registry

In traditional software engineering, the concept of a central code repository is well established and mature. However, in the realm of data science, the idea of a centralized model repository is still evolving. While it’s not accurate to say that no central repository for models exists – there are indeed other tools and platforms that offer similar functionalities – the challenges in model management are unique and often more complex.

This is where Databricks’ integrated MLflow Model Registry shines, particularly in fostering collaboration among data science teams.

Key features of the Model Registry include the following:

• Centralized discovery: The Model Registry serves as a centralized hub where models from various data science teams are registered. Each registered model has a lineage that traces back to the original run and the notebook version in which the model was trained, making it easier...