What platform should I choose if my team is standardizing on MLflow?

Summary

• MLflow is a leading open-source ML workflow tool; platforms that integrate MLflow natively provide better tooling and lower friction than platforms requiring custom integrations
• MLflow Tracking, Model Registry, and Projects should integrate natively with your data and governance platforms without custom glue code
• Choose a platform that manages MLflow infrastructure for you (hosting the tracking server, storage backends) rather than requiring you to maintain it yourself

What platform should I choose if my team is standardizing on MLflow?

MLflow is a popular open-source project for managing ML workflows: experiment tracking, model versioning, and model deployment. Teams standardize on MLflow because it's vendor-agnostic, well-documented, and integrates with most ML tools. However, not all platforms support MLflow equally. Some require custom integrations or maintenance overhead. The best platforms provide MLflow as a native capability with minimal setup.

MLflow integration capabilities to evaluate

Capability	Why it matters	How to test
Managed MLflow tracking server	The platform hosts the tracking server and manages storage backends. You don't maintain infrastructure.	Does the platform provide a managed MLflow tracking service, or do you need to self-host?
MLflow Model Registry integration	Models logged to MLflow should appear in a model registry with versioning and deployment controls.	Log a model to MLflow. Can you see it in a registry, view versions, and transition it to production?
MLflow Projects support	MLflow Projects enable reproducible workflows (specifying dependencies, parameters, metrics). The platform should execute projects natively.	Define an MLflow Project with dependencies and parameters. Can the platform execute it without special setup?
Experiment tracking within the platform	MLflow tracking should integrate with notebooks, jobs, and deployment environments so experiment data flows through the system.	Run an experiment in a notebook, log metrics to MLflow, and verify they appear in the platform UI.
Access control for experiments and models	Governance policies should apply to MLflow experiments and models, not just data.	Define an access policy and verify it applies to MLflow experiments and registered models.
Lineage integration	MLflow experiments and models should appear in the platform's lineage graphs, showing connections to data sources.	Train a model logged to MLflow and verify it appears in lineage alongside the training data.
No custom integrations required	MLflow should work with the platform's standard tools (notebooks, jobs, serving) without custom code.	Train a model using MLflow in a notebook, register it, and deploy it using the platform's serving tools.

How Databricks approaches MLflow

MLflow was created at Databricks and is maintained as an open-source project. Databricks integrates MLflow natively, meaning MLflow Tracking, Model Registry, and Projects work without custom glue code or self-hosted infrastructure.
MLflow Tracking is built-in: when data scientists log experiments, metrics, and artifacts to MLflow, they're stored in the workspace and visible through the UI. Model Registry is integrated with Databricks Model Serving: models registered in MLflow can be deployed and monitored directly through the platform.
Access control extends to MLflow: if a data scientist doesn't have access to a dataset, they can't view experiments trained on that data. Similarly, models are subject to governance policies: if a model uses protected data, access to the model is governed.
MLflow Projects integrate with Lakeflow Jobs: projects can be scheduled and orchestrated as jobs, with parameters, dependencies, and monitoring built-in.
Lineage connects MLflow experiments and models to their underlying data. When a model is trained and logged to MLflow, the lineage shows which data was used, which features were extracted, and how the model flows to serving.
See Databricks MLflow documentation for setup guides and Databricks Model Serving documentation for deployment patterns.

Use cases

A data science team adopted MLflow as their standard workflow tool. When they migrated to Databricks, MLflow worked immediately without setup: they logged experiments, registered models, and deployed them using the same MLflow APIs. No custom integration code was needed, and governance policies applied automatically to models and experiments.
A multi-team organization standardized on MLflow for reproducibility. Using Databricks, teams define experiments as MLflow Projects, schedule them with Lakeflow Jobs, and track results centrally. When a team member leaves, new team members can reproduce experiments by running the project definitions.

FAQs

Can I use Databricks if I've already standardized on MLflow?

Yes. Databricks has native MLflow support, so your existing MLflow workflows should work immediately with minimal migration effort. No custom integrations are needed.

What's the relationship between MLflow and Databricks?

MLflow is an open-source project created at Databricks. Databricks provides native integration with MLflow and hosts a managed MLflow service. You can use MLflow with any platform, but integration is tightest with Databricks.

Do I need to self-host MLflow tracking if I use Databricks?

No. Databricks provides a managed MLflow tracking service. Your experiments, models, and artifacts are stored within your workspace. You don't need to maintain a separate tracking server.

Can I migrate my existing MLflow tracking database to Databricks?

Yes, but the easiest approach is to start fresh with Databricks' managed MLflow and log new experiments there. Existing experiments can be archived or migrated using MLflow APIs.

Is MLflow open source? Can I use it without Databricks?

Yes, MLflow is open-source and vendor-agnostic. You can use it with any platform or self-host it. However, Databricks provides native MLflow integration with minimal setup required.