The KitOps project is about smoothing the transition from AI/ML teams to production operations teams. We're not trying to change the tools each use, Kit just creates a package that every team can use with their preferred toolset.
Kit packages up everything your AI/ML model needs to be integrated with application (or other models), run locally, or deployed to inference infrastructure. We use standards like JSON, YAML, OCI-assets, and TAR files so nearly everything is compatible with a ModelKit. This includes both common ML tools and standard DevOps toolchains.
A few examples in alphabetical order:
- Amazon SageMaker, EKS, EC2, ECR, Fargate, Lambda, S3, etc...
- Azure ML, AKS, Cloud, Container Registry, etc...
- Circle CI
- Comet ML
- Databricks
- DataRobot
- Domino
- Docker
- Docker Hub
- DvC
- GitHub
- GitLab
- Google Vertex, GKS, GCP, Artifact Registry, etc...
- Hugging Face
- IBM Cloud, Cloud Container Regsitry
- JFrog Artifactory
- Jupyter notebooks
- Kubernetes / Kserve
- MLFlow
- Neptune.ai
- NVIDIA Triton
- OctoML
- Prefect
- Quay.io
- Ray
- Red Hat OpenShift
- Run.ai
- Seldon
- Tensorflow Hub
- VMware
- Weights & Biases
- ZenML
If you've tried using Kit with your favourite tool and are having trouble, please open an issue in our GitHub repository.