MLflow Model Registry
An open source platform for the machine learning lifecycle.
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)
✅ 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
Unlike cloud-specific registries, MLflow is portable and can be used across different environments.
💻 Platforms
🔌 Integrations
💰 Pricing
Free tier: Open source and free to use.
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