Databricks MLflow is an open-source platform designed to manage the entire machine learning (ML) lifecycle, from experimentation and tracking to deployment and monitoring. It provides a centralized repository for ML models and associated artifacts, making collaborating, reproducing experiments, and managing model versions easier.

Key features and benefits of using MLflow on Databricks:

• Experiment Tracking: Log parameters, metrics, and artifacts (e.g., models, plots) to keep track of ML experiments and compare results.
• Model Registry: A centralized model store for managing model versions, transitions (staging, production, archived), and model lineage.
• Deployment: Easily deploy models to various targets, such as REST endpoints, batch inference jobs, or streaming platforms.
• Integration with Databricks: Seamless integration with Databricks notebooks, jobs, and data stores for a streamlined ML workflow.
• Scalability: Leveraging Databricks’ distributed computing capabilities for training and deploying large-scale ML models.

How Databricks extends MLflow:

• Managed MLflow: A fully managed version of MLflow on the Databricks platform, offering enhanced reliability, security, and scalability.
• MLflow Model Registry in Unity Catalog: Centralized model governance, cross-workspace access, lineage, and deployment.
• Enhanced UI and collaboration features: Streamlined UI for managing experiments, models, deployments, and team collaboration features.

Getting started with MLflow on Databricks:

• • Tutorials and documentation: Databricks provides extensive documentation and tutorials to help you start with MLflow. You can find them here: ML lifecycle management using MLflow: https://docs.databricks.com/en/mlflow/index.html.
  • Tutorial: End-to-end ML models on Databricks: https://docs.databricks.com/en/mlflow/end-to-end-example.html
• Databricks Community Edition: If you’re new to Databricks, you can start with the free Community Edition to explore MLflow and its capabilities.