Why Use KitOps?

KitOps is the market's only open source, standards-based packaging and versioning system designed for AI/ML projects. Using the OCI standard allows KitOps to be painlessly adopted by any organization using containers and enterprise registries today (see a partial list of compatible tools).

KitOps has been downloaded over 20,000 times in just the last three months. Users often use it as a:

• Secure and immutable packaging and versioning standard that is compatible with their existing container registry
• Point-of-control between development and production to enforce consistency in packaging and documentation
• Catalogue of meaningful AI/ML project versions for regulatory compliance or change tracking
• Mechanism to simplify and unify the creation of containers or Kubernetes deployment YAML

NOTE

The goal of KitOps is to be a library of versioned packages for your AI project, stored in an enterprise registry you already use.

The Problem

There is no standard and versioned packaging system for AI/ML projects. Today each part of the project is kept somewhere different:

• Code in Jupyter notebooks or (if you're lucky) git repositories
• Datasets in DvC or storage buckets like S3
• Configuration in Jupyter notebooks, feature stores, MLOps tools, or ...
• Pipeline definitions in proprietary tools

This makes it difficult to track which versions of code, model, and datasets go together. It makes building containers harder and managing in-production AI/ML projects riskier.

Teams that use ModelKits report saving between 12 and 100 hours per AI/ML project iteration. While security and compliance teams appreciate that all AI/ML project assets are packaged together for each version and stored in an already secured and auditable enterprise container registry.

Suddenly tough questions like these are easy to answer:

• Where did the model come from? Can we trust the source?
• When did the dataset change? Which models were trained on it?
• Who build and signed off on the model?
• Which model is in production, which is coming, and which has been retired?

The Solution

Kit's ModelKits are the better solution:

• Combine models, datasets, code and all the context teams need to integrate, test, or deploy:
  • Training code
  • Model code
  • Serialized model
  • Training, validation, and other datasets
  • Metadata
• Let teams reuse their existing container registries by packaging everything as an OCI-compliant artifact
• Support unpacking only a piece of the model package to your local machine (saving time and space)
• Remove tampering risks by using an immutable package
• Reduce risks by including the provenance of the model and datasets

Use kit pack to package up your Jupyter notebook, serialized model, and datasets (based on a Kitfile).

Then kit push it to any OCI-compliant registry, even a private one.

Most people won't need everything, so just kit unpack only the layers you need (e.g., only model and datasets, or only code and docs) from the remote registry. Or, if you need everything then a kit pull will grab everything.

Finally package it all up as a container or Kubernetes deployment.

Check out our getting started doc, see the power and flexibility of our CLI commands, or learn more about packaging your AI/ML project with ModelKits and even making them deployable.