Comet
Experiment tracking, model registry & production monitoring for ML teams
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
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.
When teams consider ModelDB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
Use Docker Compose (`docker-compose -f docker-compose-all.yaml up`) or deploy via the Helm chart for Kubernetes.
Python and Scala client libraries are provided.
PostgreSQL is the default relational database; other SQL databases are supported through Hibernate.
Yes, native integrations allow automatic logging of metrics and artifacts from these frameworks.
Contact modeldb@verta.ai for information about a managed offering.
Project at a glance
DormantLast synced 4 days ago
