Comet
Experiment tracking, model registry & production monitoring for ML teams
ModelDB
Version, track, and manage ML models end-to-end
ModelDB provides reproducible model versioning, experiment tracking, and lifecycle management with Docker/Kubernetes deployment, Python/Scala clients, dashboards, and flexible metadata logging.
Overview
Highlights
Docker and Kubernetes ready deployments
Python and Scala client libraries
Interactive dashboards for performance reporting
Pluggable storage with Git‑like model operations
Pros
- Ensures reproducible ML workflows
- Rich metadata logging and visual dashboards
- Flexible deployment on Docker or Kubernetes
- Supports popular frameworks like TensorFlow and PyTorch
Considerations
- Requires infrastructure setup (Docker/K8s, database)
- Limited client language support (Python, Scala only)
- Learning curve for full lifecycle features
- UI may lack polish compared to commercial SaaS tools
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Fit guide
Great for
- Teams needing on‑prem model versioning and experiment tracking
- Organizations that require compliance and reproducibility
- MLOps pipelines that integrate with TensorFlow or PyTorch
- Projects that prefer customizable storage backends
Not ideal when
- Simple scripts without a need for model version control
- Teams looking for a fully managed SaaS solution
- Environments that rely on languages other than Python or Scala
- Organizations without database or container orchestration expertise
How teams use it
Track experiments with hyperparameters and metrics
Data scientists log each run, compare accuracy, and reproduce results easily.
Create performance dashboards for model monitoring
Stakeholders view real‑time metrics and trends through the web UI.
Manage model lifecycle from development to production
Models are versioned, stored, and promoted across environments with full metadata.
Integrate automatic logging into TensorFlow training pipelines
Training scripts emit metrics and artifacts to ModelDB without manual code.
Tech snapshot
Java34%
Python27%
TypeScript18%
Scala16%
JavaScript2%
CSS1%
Frequently asked questions
How do I install ModelDB?
Use Docker Compose (`docker-compose -f docker-compose-all.yaml up`) or deploy via the Helm chart for Kubernetes.
Which programming languages are supported?
Python and Scala client libraries are provided.
What storage backends can ModelDB use?
PostgreSQL is the default relational database; other SQL databases are supported through Hibernate.
Can ModelDB be used with TensorFlow or PyTorch?
Yes, native integrations allow automatic logging of metrics and artifacts from these frameworks.
Is a hosted version of ModelDB available?
Contact modeldb@verta.ai for information about a managed offering.
Project at a glance
Dormant- Stars
- 1,745
- Watchers
- 1,745
- Forks
- 288
LicenseApache-2.0
Repo age9 years old
Last commitlast year
Primary languageJava
Last synced 4 hours ago