Which function in Databricks is primarily used for managing machine learning models?

The MLflow model registry function is designed specifically for managing machine learning models throughout their lifecycle. It provides a centralized location to store, version, and organize models, making it easier to track model evolution, handle model deployment, and manage various stages of the machine learning workflow.

Using the model registry, data scientists and machine learning engineers can register new models, maintain multiple versions of the same model, and annotate models with metadata such as description, parameters, and metrics. This is crucial for managing models in collaborative environments where multiple users may be working on different versions or different projects.

In contrast, the other options refer to important aspects of the machine learning process but do not specifically address model management. Data versioning involves keeping track of the data that models leverage, parameter tuning focuses on optimizing those models by adjusting hyperparameters, and the training function is concerned with the actual process of training models on datasets. While each is vital for building and optimizing machine learning systems, they do not serve the specific management function that the MLflow model registry provides.