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MLflow Model Registry

An open source platform for the machine learning lifecycle.

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Overview

The MLflow Model Registry provides model lineage, versioning, stage transitions, and annotations. It is a central hub for managing the lifecycle of MLflow Models, from experimentation to production. It allows teams to track model versions, move models between stages like 'staging' and 'production', and annotate models with comments and tags.

✨ Key Features

  • Model Versioning
  • Model Staging (e.g., Staging, Production)
  • Model Lineage (tracking the experiment and run that produced the model)
  • Annotations and Descriptions
  • API for managing and querying models

🎯 Key Differentiators

  • Open source and vendor-neutral
  • Large and active community
  • Flexibility to run on any cloud or on-premise

Unique Value: Provides an open and flexible platform for managing the entire machine learning lifecycle, avoiding vendor lock-in.

🎯 Use Cases (4)

Tracking and managing machine learning models Collaborative model development CI/CD for machine learning Model governance and auditing

✅ Best For

  • A/B testing of models
  • Archiving and retiring old models

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Real-time model monitoring (MLflow focuses more on the registry and tracking aspects)

🏆 Alternatives

Amazon SageMaker Model Registry Google Vertex AI Model Registry Azure Machine Learning Model Registry

Unlike cloud-specific registries, MLflow is portable and can be used across different environments.

💻 Platforms

Web API

🔌 Integrations

Databricks Amazon SageMaker Microsoft Azure Machine Learning Kubernetes PyTorch TensorFlow Scikit-learn

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: Open source and free to use.

Visit MLflow Model Registry Website →