You can find a lot more info on this at this MLflow product page, including a comparison table at the bottom. I'd summarize that comparison as: Databricks provides three key things in its managed MLflow service.
- Security: MLflow experiments, models, model stages, and artifacts use the same access control models as other Databricks objects (clusters, jobs, etc.). This makes it much easier for admins to manage security in their holistic data platform, rather than implementing ACLs separately for ML.
- Scalability: Our managed Tracking Server and Model Registry are hosted and scaled for you, and registries can support millions of models. You don't need to implement a highly available and scalable service yourself.
- Integrations: Workspace integrations improve the MLflow user experience (e.g., notebook Runs sidebar). Workflow integrations simplify environment management (Databricks Runtimes + Libraries), compute resources (Clusters), and automation (Jobs, Model Registry webhooks). You don't need to build these integrations yourself in a DIY or piecemeal platform.
