Jozu Hub Compatible Tools

Jozu Hub was designed from the ground-up to be compatible with the DevOps and AI/ML development tools that enterprises, governments, and research labs use.

At its heart is the open source KitOps project, part of the Cloud Native Computing Foundation (CNCF). KitOps defines a ModelKit format based on the Open Container Initiative (OCI) standards — making Jozu compatible with nearly any DevOps, MLOps, or model hosting tool.

JOZU WORKS WITH YOUR STACK

Whether you're running on AWS, OpenShift, or bare metal — or using GitHub, Hugging Face, or Jenkins — Jozu fits right in.

The most common integration points for Jozu Hub are:

• Kubernetes
• OCI registries
• CI/CD and Workflow tools
• AI/ML development tools

Kubernetes Distributions (A-Z)

Jozu Hub is Kubernetes-native and installs cleanly into any modern distribution. Whether you're deploying in a managed cloud service or your own datacenter, Jozu runs securely and efficiently on the platforms your DevOps team already knows.

• Amazon EKS
• Azure AKS
• Canonical Kubernetes
• DigitalOcean Kubernetes
• Google GKE
• IBM Cloud Kubernetes Service
• Mirantis Kubernetes Engine
• Oracle OKE
• Rancher Kubernetes Engine (RKE)
• Red Hat OpenShift
• VMware Tanzu Mission Control / Kubernetes Grid

DEPLOY YOUR PROJECTS ANYWHERE

Jozu Hub's generated inference containers can be deployed anywhere that containers can run.

OCI Registries

Jozu uses OCI-compliant registries to store and distribute model packages (ModelKits). You can push, pull, and scan models using any registry that supports OCI 1.1.

• 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
• Jozu Hub
• Red Hat Quay.io
• Sonatype Nexus
• Zed Registry

MLOps Tools (A-Z)

Jozu fits neatly into your machine learning stack by complementing your existing MLOps tools. From experiment tracking and training to packaging and deployment, Jozu provides traceability, security, and speed across your AI workflows.

• Clear ML
• Comet ML
• Databricks
• DvC
• Hugging Face
• Jupyter notebooks
• Kubeflow
• Marimo
• MLFlow
• ModelScan
• Neptune.ai
• NVIDIA Triton and Run.ai
• OctoML
• Prefect
• Ray
• Red Hat InstructLab
• Red Hat OpenShift AI
• Tensorflow Hub
• Weights & Biases
• ZenML

Pipeline & Storage Tools (A-Z)

Jozu ModelKits are designed to integrate directly into your DevOps pipelines. Whether you're building with GitHub Actions, storing in S3, or deploying with Argo or Tekton, Jozu supports smooth automation across your infrastructure.

• Amazon S3
• Argo CD
• BitBucket Pipelines
• Circle CI
• Dagger
• Flux CI/CD
• Git
• Git LFS
• GitHub Actions
• GitLab Pipelines
• Jenkins CI/CD
• Kubeflow
• Spinnaker
• Tekton
• Travis CI

For questions, or when you're ready to get started, please contact [email protected].