What functionality does MLflow provide in Databricks?

MLflow is a key component within Databricks that specifically addresses the machine learning lifecycle. It provides a suite of tools that facilitate various stages of machine learning projects, including tracking experiments, packaging code into reproducible runs, and managing and deploying models.

By offering functionalities such as experiment tracking, which allows data scientists to log parameters, metrics, and artifacts associated with their machine learning experiments, MLflow enables teams to monitor progress and performance effectively. It also supports model versioning and provides a registry for easy deployment of models into production environments, which is essential for maintaining consistency and reliability in model-based applications.

The other options, while relevant to data engineering and analytics, do not align with the primary purpose of MLflow. Real-time data ingestion pertains more to data streaming frameworks and technologies rather than machine learning management. Data integration with relational databases relates to ETL processes and data preparation rather than lifecycle management in ML. Automated testing of ETL jobs focuses on data workflows and does not involve machine learning model management, which is the core function of MLflow.