# KitOps

> Discover KitOps: an open-source DevOps tool that packages and versions your AI/ML models, datasets, code, and configurations into reproducible artifacts called ModelKits. Simplify your AI pipeline with standardized packaging and deployment.

## Table of Contents

### Getting started

- [What is KitOps? | Open-Source AI/ML Packaging and Deployment](/docs/overview.md): KitOps is an open-source standard for packaging, versioning, and deploying AI/ML models. Built on OCI, it simplifies collaboration across data science, DevOps, and software teams.
- [KitOps Use Cases - Secure Model Delivery & Versioning](/docs/use-cases.md): Learn how organizations use KitOps to securely package, track, and deploy AI/ML models through CI/CD, with security scanning, tagging, and rollback.
- [KitOps Tutorial - Open Source AI/ML Packaging CLI](/docs/get-started.md): Learn how to install and use the Kit CLI to package, version, and share AI/ML models using ModelKits. Follow our step-by-step guide for setup and deployment.
- [Import Hugging Face Models into ModelKits](/docs/hf-import.md): Learn how to use the KitOps CLI to import Hugging Face models into ModelKits. Use KitOps to create a curated, private model registry behind your firewall.
- [Deploying AI with KitOps - Kubernetes, Containers, and Local LLMs](/docs/deploy.md): Learn how to securely deploy AI/ML models to Kubernetes, container runtimes, or run LLMs. Includes init containers, custom Dockerfiles, and dev workflows.
- [ModelKit Security - Integrity, Signing, and Verification](/docs/security.md): Learn how KitOps ModelKits ensure AI/ML model integrity with built-in SHA-256 verification, optional Cosign signing, and integration with transparency logs. Secure your AI supply chain from development to deployment.
- [Why Use KitOps - Standards-Based AI/ML Packaging](/docs/why-kitops.md): Discover why KitOps is the leading open-source solution for packaging, versioning, and deploying AI/ML models. Built on OCI standards and compatible with your existing tools.
- [KitOps FAQ](/docs/faq.md): Frequently asked questions about KitOps and ModelKit packaging, including symlink handling and context boundaries.

### ModelKit

- [ModelKit Overview - OCI Packaging for AI/ML Projects](/docs/modelkit/intro.md): Learn how ModelKit standardizes the packaging of models, datasets, and code for AI/ML workflows. OCI-compliant, versioned, and easy to use across registries and tools.
- [ModelKit Specification](/docs/modelkit/spec.md): Dive into the ModelKit specification. Understand the structure and components of a ModelKit, including models, datasets, configurations, and code packaged as OCI artifacts.

### Kitfile

- [Kitfile Format - Define AI/ML Projects for KitOps ModelKits](/docs/kitfile/kf-overview.md): Learn how to create and use a Kitfile to securely package AI/ML projects for enterprise use. Define models, code, and datasets using a simple YAML format for versioned sharing and deployment.
- [Kitfile AI/ML Packaging Manifest Format Reference](/docs/kitfile/format.md): Dive deep into the Kitfile format. Learn the syntax, structure, and key components for defining ModelKits.

### Kit CLI

- [Kit CLI Reference](/docs/cli/cli-reference.md): Explore the Kit CLI command reference. Get detailed information on commands available for building, versioning, pushing, pulling, and running ModelKits within your AI/ML projects.
- [Install KitOps CLI - macOS, Windows, Linux](/docs/cli/installation.md): Learn how to install KitOps, the open-source CLI for packaging and managing AI/ML models with ModelKits. Supports macOS, Windows, Linux, and source builds.

### Kit Python Library

- [KitOps Python Library](/docs/pykitops.md): Discover PyKitOps, the Python library for working with ModelKits. Integrate KitOps into your Python-based AI workflows.
- [Before you begin with PyKitOps](/docs/pykitops/before-you-begin.md)
- [How-to Guides](/docs/pykitops/how-to-guides.md): Follow practical how-to guides for PyKitOps. Learn step-by-step methods for managing and deploying ModelKits in Python.
- [Reference](/docs/pykitops/reference.md): Access the PyKitOps reference documentation. Find detailed explanations of functions, classes, and APIs available for ModelKit management.

### Kit TypeScript Library

- [kitops-ts](/docs/kitops-ts.md)
- [Getting Started](/docs/kitops-ts/getting-started.md)
- [API Reference](/docs/kitops-ts/api-reference.md)
- [Type Definitions](/docs/kitops-ts/types.md)
- [Examples](/docs/kitops-ts/examples.md)

### Integrations

- [KitOps Integrations - Compatible Tools & Registries](/docs/integrations/integrations.md): Discover all the tools and platforms that work with KitOps ModelKits, including OCI registries, MLOps tools, CI/CD platforms, cloud services, and model tracking systems.
- [MLFlow and KitOps ModelKits](/docs/integrations/mlflow.md): Use KitOps PyKitOps Python library to automate ModelKit creation with MLFlow.
- [Using KitOps ModelKits from a CI/CD Tool](/docs/integrations/cicd.md): Use KitOps ModelKits to add AI/ML to your CI/CD tool's pipelines.
- [Deploying a ModelKit to Kubernetes with the Kit Init Container](/docs/integrations/k8s-init-container.md)
- [Integrating KitOps with KServe](/docs/integrations/kserve.md): Learn how to integrate KitOps ModelKits with KServe using a ClusterStorageContainer.
- [Kubeflow Pipeline Components for ModelKits](/docs/integrations/kubeflow.md): Learn how to integrate KitOps ModelKits with Kubeflow Pipelines using push-modelkit and unpack-modelkit components.
- [Using KitOps to Create ModelPacks](/docs/integrations/modelpack.md): Use KitOps to create CNCF ModelPack-compliant packages.


### Other

- [Contributing](/docs/kitops-ts/contributing.md)
- [Kit Commands](/docs/pykitops/reference/kit.md)
- [Kitfile Class](/docs/pykitops/reference/kitfile.md)
- [ModelKitManager Class](/docs/pykitops/reference/manager.md)
- [ModelKitReference Class](/docs/pykitops/reference/reference.md)
- [Next Steps with Kit](/docs/next-steps.md): Next steps in your KitOps journey. Explore advanced features, best practices, and how to optimize your workflow.
- [Redirecting...](/docs.md)
- [UserCredentials Class](/docs/pykitops/reference/user.md)