Why Use KitOps?
AI/ML projects are complex. KitOps makes them manageable.
From datasets and models to configuration and deployment artifacts, every AI/ML project has many moving parts — and no consistent way to version, package, or deploy them together. KitOps solves this with an open-source, OCI-based standard for packaging and sharing complete AI/ML projects.
The Problem: No Standard Way to Package AI/ML Projects
Most AI/ML projects are scattered across tools:
- Code in Jupyter notebooks or Git
- Datasets in S3 buckets or DVC
- Configs hidden in pipelines or feature stores
- Pipelines in proprietary systems
Each has its own package and its own version - but nothing ties the whole project together, and getting the wrong combination means delays, risk, and failure.
This makes it hard to answer critical questions like:
- What code and data was used to train this model?
- Who built and approved it?
- What version is running in production?
- What changed — and when?
It also slows down collaboration between teams and introduces risks for security, compliance, and reproducibility.
The KitOps Solution
KitOps brings DevOps-style packaging to AI/ML workflows.
Using KitOps, you can:
- Package model, code, datasets, metadata — into a single, versioned ModelKit
- Store and share using any OCI-compliant registry (Docker Hub, GitLab, Harbor)
- Unpack only what you need (e.g., just code, just dataset)
- Run reproducible pipelines across development, testing, and production
- Reduce risk with immutable, traceable AI project artifacts
KitOps is designed for speed, security, and interoperability.
➡️ See compatible tools
How It Works
- Write a Kitfile to describe your project contents
- Run
kit packto create a ModelKit - Use
kit pushto upload to any OCI registry - Share and deploy across teams using
kit pullorkit unpack
Built with standards from the Open Container Initiative (OCI), KitOps is compatible with the registries, pipelines, and serving infrastructure your organization already uses.
➡️ Install the CLI
Trusted by Teams
KitOps has been downloaded by hundreds of thousands of users, and is in production use in high security organizations, governments, enterprises, and clouds.
Teams use it to:
- Create a central source of truth for models and datasets
- Speed up model deployment with standardized artifacts
- Track changes and approvals for compliance and audits
- Enforce consistency across environments
➡️ Try our simple Get Started
KitOps Helps You Answer:
- What data trained this model?
- Which version is deployed where?
- Who signed off on it — and when?
- What changed between model version 3 and 4?
➡️ See how security is built-in
How KitOps Compares
KitOps doesn’t replace your favorite AI/ML tools — it complements them with the secure, standardized packaging they are missing.
KitOps and MLOps Platforms
Tools like MLflow, Weights & Biases, and others are great for tracking experiments. But they aren’t designed to package and version full AI/ML projects for handoff across teams or deployment pipelines.
KitOps:
- Creates secure, immutable packages
- Stores them in standard OCI registries for security
- Integrates with any DevOps or MLOps tool
- Is free, open source, and governed by the independent CNCF organization
➡️ Integrate KitOps with experiment trackers
KitOps and Jupyter Notebooks
Notebooks are great for prototyping, but poor at versioning and reproducibility.
ModelKits:
- Package serialized models, code, and data
- Preserve state outside the notebook
- Let others reuse your work without opening a notebook
👉 Tip: Add a Kitfile to your notebook project and run kit pack at the end of each run.
➡️ Add KitOps to your notebook
KitOps and Containers
Containers are ideal for deployment — but they’re awkward for tracking datasets, configs, or experiment metadata.
ModelKits:
- Can include Dockerfiles and container artifacts
- Are easier for data teams to create than full containers
- Provide a clean handoff point between DS and DevOps teams
- Can be turned into containers when deployment is needed
➡️ Learn about deployments with KitOps
KitOps and Git
Git is built for code — not large binary files like models or datasets.
ModelKits:
- Handle binaries gracefully (no LFS nightmares)
- Keep everything in sync
- Can still include code snapshots from Git when needed
KitOps and Ad-Hoc Dataset Storage
Datasets are often scattered across S3 buckets, local drives, databases, or BI tools. That makes it hard to answer questions like:
- Which model used this dataset?
- Did this dataset change — and when?
- Is this data safe to use?
ModelKits:
- Version datasets alongside model code and configs
- Reduce duplication and risk
- Support reproducibility and audit trails
Recap: What Makes KitOps Unique
- Full AI/ML project packaging
- Built on OCI standards
- Works with your existing registries and CI/CD
- Open source, vendor-neutral
- Designed for team collaboration and governance