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 MLflow

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

MLflow Basics

Learn how to perform common tasks in MLflow

Guides

Autologging guide for effortless model tracking

Model Signatures and type validation in MLflow

Model Deployment Quickstart

Hyperparameter tuning with MLflow

MLflow Models Introduction

Learn about MLflow Model-related topics

Guides

Introduction to Custom Python Models

Model dependency management in MLflow

Model Signatures and type validation

Traditional ML

Get started with MLflow's Traditional ML integrations

Quickstarts and Guides

Scikit-learn introduction with MLflow

XGBoost usage with MLflow

Spark ML guide

Prophet Time Series Forecasting with MLflow

Deep Learning

Get started with MLflow's Deep Learning Library integrations

Guides and Tutorials

TensorFlow

PyTorch

Keras

spaCy for NLP workflows

Traditional ML and Deep Learning with MLflow

MLflow provides comprehensive support for traditional machine learning and deep learning workflows. From experiment tracking and model versioning to deployment and monitoring, MLflow streamlines every aspect of the ML lifecycle. Whether you're working with scikit-learn models, training deep neural networks, or managing complex ML pipelines, MLflow provides the tools you need to build reliable, scalable machine learning systems.

Explore the core MLflow capabilities and integrations below to enhance your ML development workflow!

Tracking & Experiments

Track experiments and manage your ML development

Core Features

MLflow Tracking provides comprehensive experiment logging, parameter tracking, metrics visualization, and artifact management.

Key Benefits:

• Experiment Organization: Track and compare multiple model experiments
• Metric Visualization: Built-in plots and charts for model performance
• Artifact Storage: Store models, plots, and other files with each run
• Collaboration: Share experiments and results across teams

Guides

Getting Started with Tracking

Advanced Tracking Features

Autologging for Popular Libraries

Model Registry

Manage model versions and lifecycle

Core Features

MLflow Model Registry provides centralized model versioning, stage management, and model lineage tracking.

Key Benefits:

• Version Control: Track model versions with automatic lineage
• Stage Management: Promote models through staging, production, and archived stages
• Collaboration: Team-based model review and approval workflows
• Model Discovery: Search and discover models across your organization

Guides

Model Registry Introduction

Registering Your First Model

Model Deployment

Deploy models to production environments

Core Features

MLflow Deployment supports multiple deployment targets including REST APIs, cloud platforms, and edge devices.

Key Benefits:

• Multiple Targets: Deploy to local servers, cloud platforms, or containerized - enronments
• Model Serving: Built-in REST API serving with automatic input validation
• Batch Inference: Support for batch scoring and offline predictions
• Production Ready: Scalable deployment options for enterprise use

Guides

Model Deployment Overview

Local Model Serving

Cloud Deployment Options

Kubernetes Deployment

ML Library Integrations

Explore Native MLflow ML Library Integrations

Scikit-learn

XGBoost

TensorFlow

PyTorch

Keras

Spark MLlib

Model Evaluation

Evaluate and validate your ML models

Core Features

MLflow Evaluation provides comprehensive model validation tools, automated metrics calculation, and model comparison capabilities.

Key Benefits:

• Automated Metrics: Built-in evaluation metrics for classification, regression, and - mo
• Custom Evaluators: Create custom evaluation functions for domain-specific metrics
• Model Comparison: Compare multiple models and versions side-by-side
• Validation Datasets: Track evaluation datasets and ensure reproducible results

Guides

Learn how to evaluate your ML models with MLflow

Discover custom evaluation metrics and functions

Compare models with MLflow Model Comparison

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

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.

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, 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.

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.