Vendor Name | MLflow |
Stand-alone vs. Platform | Stand-alone as an open-source project |
Delivery Model | Available both as the open-source software and a managed solution as a part of the Databricks platform. |
Clouds Supported | The open-source version is compatible on all clouds |
Pricing Model | For the Databricks platform, it is usage-based (consumption) pricing |
Service Level Guarantees | Open-source: None |
Support | Open-source: community |
SSO, ACL | Open-source: None |
Security and Compliance | Open-source: None |
Setup | Open-source: |
Flexibility, Speed, and Accessibility | Open-source: Metadata structure is static. MLflow doesn't support nested parameter structures or custom log/artefact subfolders to organize model metadata. |
Model Versioning, Lineage, and Packaging | Supports: |
Log and Display of Metadata | Supports: |
Comparing Experiments and Models | Comparison plots available: |
Organizing and Searching Experiments and Models | Open-source: |
Model Review, Collaboration, and Sharing | Open-source: Ability to transition models between production stages (Staging, Production or Archived) via MLflow API. |
CI/CD/CT Compatibility | Open-source: Ability to integrate inside CT workflows, but you need to implement it. |
Integrations | Open-source: MLflow integrates with many libraries from the ML ecosystem, providing autologing and model packaging for pytorch, tensorflow, sklearn, xgboost and other libraries. |
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Vendor |
Demo link |
Stand-alone vs. Platform |
Delivery Model |
Clouds Supported |
Pricing Model |
Service Level Guarantees |
Support |
SSO, ACL |
Security and Compliance |
Setup |
Flexibility, Speed, and Accessibility |
Model Versioning, Lineage, and Packaging |
Log and Display of Metadata |
Comparing Experiments and Models |
Organizing and Searching Experiments and Models |
Model Review, Collaboration, and Sharing |
CI/CD/CT Compatibility |
Integrations |