Which feature does Databricks provide for tracking machine learning experiments?

MLflow is the feature provided by Databricks specifically designed for tracking machine learning experiments. It enables users to manage the entire machine learning lifecycle, including experimentation, reproducibility, and deployment. With MLflow, data scientists and machine learning engineers can log their parameters, metrics, and artifacts, which allows for better tracking of experiments over time. This capability supports collaborative work and enhances the workflow by making it easier to compare different experiments and understand their outcomes.

The other options do not serve the purpose of tracking machine learning experiments. DataFrames are primarily used for handling and manipulating large datasets in a structured form, while RDDs (Resilient Distributed Datasets) are a foundational data structure in Spark that allows for distributed data processing. Notebook sharing refers to the ability to share notebooks among different users for collaboration and does not inherently focus on tracking experiments or their metadata in a machine learning context. This makes MLflow distinctly suited for the task of tracking machine learning experiments within the Databricks environment.