Managed MLflow
Managing the complete machine learning lifecycle
What is Managed MLflow?
Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete machine learning lifecycle with enterprise reliability, security and scale. The latest update to MLflow is packed with innovative features that broaden its ability to manage and deploy large language models (LLMs). This enhanced LLM support is delivered through three new model flavors: Hugging Face Transformers, OpenAI functions and LangChain.
Benefits

Model development
Accelerate and simplify machine learning lifecycle management with a standardized framework for developing production-ready ML models. With managed MLflow Recipes, you can bootstrap ML projects, perform rapid iteration with ease and ship high-quality models to production at scale.

Experiment tracking
Run experiments with any ML library, framework or language, and automatically keep track of parameters, metrics, code and models from each experiment. By using MLflow on Databricks, you can securely share, manage and compare experiment results along with corresponding artifacts and code versions — thanks to built-in integrations with the Databricks Workspace and notebooks.

Model management
Use one central place to discover and share ML models, collaborate on moving them from experimentation to online testing and production, integrate with approval and governance workflows and CI/CD pipelines, and monitor ML deployments and their performance. The MLflow Model Registry facilitates sharing of expertise and knowledge, and helps you stay in control.

Model deployment
Quickly deploy production models for batch inference on Apache Spark™ or as REST APIs using built-in integration with Docker containers, Azure ML or Amazon SageMaker. With Managed MLflow on Databricks, you can operationalize and monitor production models using Databricks Jobs Scheduler and auto-managed Clusters to scale based on the business needs.
Features
See our Product News from Azure Databricks and AWS to learn more about our latest features.
Comparing MLflow offerings

How it works
MLflow is a lightweight set of APIs and user interfaces that can be used with any ML framework throughout the Machine Learning workflow. It includes four components: MLflow Tracking, MLflow Projects, MLflow Models and MLflow Model Registry
Managed MLflow on Databricks
Managed MLflow on Databricks is a fully managed version of MLflow providing practitioners with reproducibility and experiment management across Databricks Notebooks, Jobs, and data stores, with the reliability, security, and scalability of the Databricks Lakehouse Platform.