Beginner

Introduction to MLflow

Understand what MLflow is, its four core components, and why it has become the most popular open-source ML lifecycle platform.

What is MLflow?

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. Originally developed by Databricks and released in 2018, it has become the de facto standard for ML experiment tracking and model management.

MLflow is designed to work with any ML library, language, and deployment environment. Whether you're using scikit-learn, PyTorch, TensorFlow, or XGBoost, MLflow provides a unified interface for tracking and deploying your models.

The Four Components

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MLflow Tracking

Record and query experiments: parameters, metrics, code versions, and artifacts. Compare runs side-by-side in the web UI.

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MLflow Projects

Package ML code in a reusable, reproducible format. Define entry points, parameters, and environment dependencies.

🤖

MLflow Models

Package models from any framework in a standard format. Deploy to diverse serving environments with a single command.

📚

MLflow Model Registry

Centralized model store for versioning, stage transitions (Staging/Production/Archived), and collaborative model management.

Why MLflow?

  • Open-source: Free to use, no vendor lock-in. Active community with 18,000+ GitHub stars.
  • Framework-agnostic: Works with any ML framework: sklearn, PyTorch, TensorFlow, XGBoost, LightGBM, and more.
  • Language-agnostic: Python, R, Java, and REST API support.
  • Industry standard: Used by thousands of organizations from startups to Fortune 500 companies.
  • Extensible: Plugin system for custom tracking backends, artifact stores, and model flavors.

MLflow vs Alternatives

FeatureMLflowW&BNeptuneClearML
LicenseApache 2.0Freemium SaaSFreemium SaaSApache 2.0
Self-hostedYes (free)Enterprise onlyEnterprise onlyYes (free)
TrackingExcellentExcellentExcellentGood
Model RegistryBuilt-inBuilt-inVia integrationBuilt-in
Model ServingBuilt-inNoNoBuilt-in
VisualizationGoodExcellentGoodGood
CollaborationGoodExcellentGoodGood
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When to choose MLflow: Pick MLflow when you need a self-hosted, open-source solution with model serving built in. Choose W&B when visualization and collaboration are top priorities. Choose ClearML when you need a full open-source MLOps platform with pipeline orchestration.

Architecture Overview

MLflow's architecture consists of:

  • Tracking Server: REST API server that stores experiment metadata. Can use SQLite, PostgreSQL, or MySQL as backend.
  • Artifact Store: Stores model files, plots, and other artifacts. Supports local filesystem, S3, GCS, Azure Blob, HDFS.
  • Web UI: Browser-based interface for viewing experiments, comparing runs, and managing the model registry.
  • Client Libraries: Python, R, Java, and REST API for logging and querying experiments.
💡
Getting started is simple: MLflow can run entirely locally with zero infrastructure. Just pip install mlflow and start tracking experiments. You can add a proper tracking server and artifact store later as your needs grow.