Show HN: I'm building a simple way to package, collaborate, and deploy ML models (opens in new tab)

Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!?

To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits:

1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant artifact called a ModelKit. The ModelKit keeps everything in one place, tagged, and versioned, our team has found it much easier to collaborate.

2. ModelKits are modular (unlike a Docker file), you can pull only the model or datasets, for example, or grab the whole thing. Plus you can still create a dockerfile for your serialized model.

3. ModelKits provide a history of meaningful state changes for auditing and are immutable so they should be great for a secure bill-of-materials (SBOM) initiative. Full disclosure, I haven't used SBOMs myself but a friend mentioned this as a benefit so thought I'd pass it along.

4. The Kitfile is YAML and easy-to-read so even people who don't understand the ins-and-outs of ML development can find what they need to integrate models into their apps, test models with validation data, or deploy models to their inference engine of choice.

We're still early in the development process and are really interested in collecting community feedback.

You can learn more about KitOps at https://kitops.ml

Or checkout the source code here: https://github.com/jozu-ai/kitops