ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.
ModelDB
ModelDB tracks machine learning model versions, metadata, and experiments.

About ModelDB
ModelDB is an open-source system for versioning machine learning models, including code, data, config, and environment, and tracking metadata across the model lifecycle. It supports experiment management, performance dashboards, and tracking from development through deployment and monitoring.
It provides Python and Scala clients for logging from training code and can be deployed with Docker Compose or Kubernetes. Local setup requires Docker and a backend config file, and the Python client is installed via the verta package.
Key features
- Version models including code, data, config, and environment
- Log metrics, artifacts, tags, hyperparameters, and user information
- Git-like operations on models
- Dashboards for model performance and reporting
- Python and Scala clients for logging from training code
- Deploys with Docker Compose or Kubernetes via Helm
- Versions code, data, config, and environment for reproducibility
- Supports Docker Compose and Kubernetes deployment options
- Works with TensorFlow and PyTorch with Python and Scala clients
- Docker Compose setup requires backend/config/config.yaml to be present
- Hosted use requires contacting [email protected]
ModelDB at a glance
- Website
- github.com/VertaAI/modeldb
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
ModelDB alternatives
Tools and sites you could use instead, ranked by how closely they match.
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The Hydrosphere repository describes an MLOps platform for machine learning operations.
DJL is a Java framework for running deep learning models across different engines.
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ModelDB FAQ
How do I start ModelDB locally?
Is there a hosted version of ModelDB?
Which programming languages have ModelDB clients?
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