Skip to main content

MLflow: A Tool for Managing the Machine Learning Lifecycle

MLflow is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.

MLflow Getting Started Resources​

If this is your first time exploring MLflow, the tutorials and guides here are a great place to start. The emphasis in each of these is getting you up to speed as quickly as possible with the basic functionality, terms, APIs, and general best practices of using MLflow in order to enhance your learning in area-specific guides and tutorials.

Learn about the core components of MLflow​

Quickstarts​

Get Started with MLflow in our 5-minute tutorial

Guides​

Learn the core components of MLflow with this in-depth guide to Tracking

Core Components

GenAI and MLflow​

Explore the comprehensive GenAI-focused support in MLflow. From MLflow Deployments for GenAI models to the Prompt Engineering UI and native GenAI-focused MLflow flavors like open-ai, transformers, and sentence-transformers, the tutorials and guides here will help to get you started in leveraging the benefits of these powerful models, services, and applications. You’ll learn how MLflow simplifies both using GenAI models and developing solutions that leverage them. Important tasks such as prompt development, evaluation of prompts, comparison of foundation models, fine-tuning, logging, and deploying production-grade inference servers are all covered by MLflow.

Explore the guides and tutorials below to start your journey!

Explore the Native MLflow GenAI Integrations​

Hugging Face Transformers

Transformers

OpenAI

OpenAI

LangChain

LangChain

LlamaIndex

LlamaIndex

Sentence Transformers

Sentence Transformers

Running MLflow Anywhere​

MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is vendor-neutral; no matter where you are doing machine learning, you have access to the MLflow's core capabilities sets such as tracking, evaluation, observability, and more.

Hosting MLflow Locally

Run MLflow server locally or use direct access mode (no server required) to run MLflow in your local environment. Click the card to learn more.

Databricks Logo

Databricks Managed MLflow is a FREE, fully managed solution, seamlessly integrated with Databricks ML/AI ecosystem, such as Unity Catalog, Model Serving, and more.

Amazon SageMaker Logo

MLflow on Amazon SageMaker is a fully managed service for MLflow on AWS infrastructure,integrated with SageMaker's core capabilities such as Studio, Model Registry, and Inference.

AzureML Logo

Azure Machine Learning workspaces are MLflow-compatible, allows you to use an Azure Machine Learning workspace the same way you use an MLflow server.

Nebius Logo

Nebius, a cutting-edge cloud platform for GenAI explorers, offers a fully managed service for MLflow, streamlining LLM fine-tuning with MLflow's robust experiment tracking capabilities.

Kubernetes Logo

You can use MLflow on your on-premise or cloud-managed Kubernetes cluster. Click this card to learn how to host MLflow on your own infrastructure.