Compatible Tools
ModelKit packages can be pushed to any OCI 1.1-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 application's 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.
Compliant OCI Registries
- Amazon Elastic Container Registry (ECR)
- Azure Container Registry
- Docker Hub
- GitHub Packages Container Registry
- GitLab Container Registry
- Google Artifact Registry
- Harbor
- IBM Cloud Container Registry
- JFrog Artifactory
- Red Hat Quay.io
Other Compatible Tools
- Amazon SageMaker
- Amazon Elastic Kubernetes Service (EKS)
- Amazon Elastic Compute Cloud (EC2)
- Amazon Fargate
- Amazon Lambda
- Amazon S3
- Argo CD
- Azure ML
- Azure Kubernetes Service (AKS)
- Azure Cloud
- Circle CI
- Clear ML
- Comet ML
- Databricks
- DataRobot
- Domino
- DvC
- Git
- Git LFS
- GitHub
- GitLab
- Google Vertex
- Google Kubernetes Service (GKS)
- Google Container Platform (GCP)
- Hugging Face
- IBM Cloud
- Jenkins CI/CD
- Jupyter notebooks
- Kubernetes
- Kserve
- Marimo
- MLFlow
- ModelScan
- Neptune.ai
- NVIDIA Triton and Run.ai
- OctoML
- Prefect
- Ray
- Red Hat InstructLab
- Red Hat OpenShift
- Red Hat OpenShift AI
- Seldon
- Sonatype Nexus
- 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.
If you've used KitOps with a product or project we've missed, please open a pull request updating this file.