ModelDB

Overview

ModelDB is an open source system for versioning machine learning models and tracking experiment metadata. It stores model artifacts, hyperparameters, and metrics in a central repository, enabling reproducibility and comparison across runs. The tool is written in Java and provides a REST API for integration with existing ML pipelines.

Best for

Best for
Teams that need a self-hosted, open source solution for model versioning and experiment metadata tracking

Use cases

• Versioning trained models and linking them to specific experiment configurations
• Logging and querying experiment metadata such as hyperparameters and evaluation metrics
• Reproducing past model runs by retrieving stored artifacts and parameters

Notes

ModelDB is an open source system for versioning machine learning models and tracking experiment metadata. It stores model artifacts, hyperparameters, and metrics in a central repository, enabling reproducibility and comparison across runs. The tool is written in Java and provides a REST API for integration with existing ML pipelines.

1,747 stars on GitHub. Last updated 2024-07-23. Licensed Apache-2.0.

Use cases

• Versioning trained models and linking them to specific experiment configurations
• Logging and querying experiment metadata such as hyperparameters and evaluation metrics
• Reproducing past model runs by retrieving stored artifacts and parameters

Pros

• Open source with a permissive license, allowing self-hosting and customization
• Provides a centralized, queryable store for experiment metadata and model artifacts
• Supports integration via REST API, fitting into diverse ML workflows

Cons

• Limited community activity with 1,747 stars and Java codebase may deter Python-centric ML teams
• Requires self-hosting and maintenance, lacking a managed cloud option
• Documentation and examples may be sparse compared to more popular experiment tracking tools

Indexed from awesome-llmops and enriched against its public facts.