Compatible Tools
ModelKit packages can be pushed to any OCI-compliant registry, whether in the cloud, on-premises, or locally. This makes ModelKits easy to find because they're in the same place as the rest of your containers and artifacts. It also makes them easy to control since the registry already includes authentication and authorization.
ModelKits themselves use standards like JSON, YAML, and TAR files so whatever MLOps or DevOps tools you're using...they'll work with ModelKits.
A few examples in alphabetical order:
- Amazon SageMaker, EKS, EC2, ECR, Fargate, Lambda, S3, etc...
- Azure ML, AKS, Cloud, Container Registry, etc...
- Circle CI
- Clear ML
- Comet ML
- Databricks
- DataRobot
- Domino
- Docker and Docker Hub
- DvC
- Git and Git LFS
- GitHub
- GitLab
- Google Vertex, GKS, GCP, Artifact Registry, etc...
- Hugging Face
- IBM Cloud, Cloud Container Registry
- JFrog Artifactory
- Jupyter notebooks
- Kubernetes / Kserve
- MLFlow
- Neptune.ai
- NVIDIA Triton, Run.ai, etc...
- OctoML
- Prefect
- Quay.io
- Ray
- Red Hat OpenShift, OpenShift AI, Quay, InstructLab, etc...
- Seldon
- Tensorflow Hub
- VMware
- Weights & Biases
- ZenML
If you've tried using Kit with your favorite tool and are having trouble, please open an issue in our GitHub repository.