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* Update compatibility list * fix typo in Quay
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| # Compatible Tools | ||
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| 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. | ||
| 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. | ||
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| ModelKits themselves use standards like JSON, YAML, and TAR files so whatever MLOps or DevOps tools you're using...they'll work with ModelKits. | ||
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| A few examples in alphabetical order: | ||
| * Amazon SageMaker, EKS, EC2, ECR, Fargate, Lambda, S3, etc... | ||
| * Azure ML, AKS, Cloud, Container Registry, etc... | ||
| * Amazon SageMaker | ||
| * Amazon Elastic Kubernetes Service (EKS) | ||
| * Amazon Elastic Compute Cloud (EC2) | ||
| * Amazon Elastic Container Registry (ECR) | ||
| * Amazon Fargate | ||
| * Amazon Lambda | ||
| * Amazon S3 | ||
| * Argo CD | ||
| * Azure ML | ||
| * Azure Kubernetes Service (AKS) | ||
| * Azure Cloud | ||
| * Azure Container Registry | ||
| * Circle CI | ||
| * Clear ML | ||
| * Comet ML | ||
| * Databricks | ||
| * DataRobot | ||
| * Domino | ||
| * Docker and Docker Hub | ||
| * Docker | ||
| * Docker Hub | ||
| * DvC | ||
| * Git and Git LFS | ||
| * Git | ||
| * Git LFS | ||
| * GitHub | ||
| * GitLab | ||
| * Google Vertex, GKS, GCP, Artifact Registry, etc... | ||
| * Google Vertex | ||
| * Google Kubernetes Service (GKS) | ||
| * Google Container Platform (GCP) | ||
| * Google Artifact Registry | ||
| * Hugging Face | ||
| * IBM Cloud, Cloud Container Registry | ||
| * IBM Cloud | ||
| * IBM Cloud Container Registry | ||
| * Jenkins CI/CD | ||
| * JFrog Artifactory | ||
| * Jupyter notebooks | ||
| * Kubernetes / Kserve | ||
| * Kubernetes | ||
| * Kserve | ||
| * Marimo | ||
| * MLFlow | ||
| * ModelScan | ||
| * Neptune.ai | ||
| * NVIDIA Triton, Run.ai, etc... | ||
| * NVIDIA Triton and Run.ai | ||
| * OctoML | ||
| * Prefect | ||
| * Quay.io | ||
| * Ray | ||
| * Red Hat OpenShift, OpenShift AI, Quay, InstructLab, etc... | ||
| * Red Hat InstructLab | ||
| * Red Hat OpenShift | ||
| * Red Hat OpenShift AI | ||
| * Red Hat Quay.io | ||
| * Seldon | ||
| * Sonatype Nexus | ||
| * Tensorflow Hub | ||
| * VMware | ||
| * Weights & Biases | ||
| * ZenML | ||
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| 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 tried using Kit with your favorite tool and are having trouble, please [open an issue](https://github.com/jozu-ai/kitops/issues/new/choose) in our GitHub repository. | ||
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| If you've used KitOps with a product or project we've missed, please open a pull request updating this file. |