Today, we are excited to extend Databricks Community Edition with hosted MLflow for free, as part of our ongoing commitment to help developers learn about machine learning lifecycle. With the Community Edition, you can try tutorials that demonstrate how to track results and experiments as you build machine learning models—a crucial stage in the machine learning model’s development lifecycle.
MLflow is an open-source platform for the machine learning lifecycle with four components: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Registry. MLflow is now included in Databricks Community Edition, meaning that you can utilize its Tracking and Model APIs within a notebook or from your laptop just as easily as you would with managed MLflow in Databricks Enterprise Edition.
I like showing off Databricks Community Edition, and I’m glad to see them extend it a bit.