From 9b0ed4e116712e4126f0f0ea229c21aeb21dd09b Mon Sep 17 00:00:00 2001 From: Gorkem Ercan Date: Wed, 25 Dec 2024 04:08:51 -0500 Subject: [PATCH] Add kitops to README.md (#626) Adding the kitops project again. It looks like it was removed in a previous commit. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b6add321..1381dcbe 100644 --- a/README.md +++ b/README.md @@ -604,6 +604,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product * [Guild AI](https://github.com/guildai/guildai) ![](https://img.shields.io/github/stars/guildai/guildai.svg?style=social) - Open source toolkit that automates and optimizes machine learning experiments. * [Hangar](https://github.com/tensorwerk/hangar-py) ![](https://img.shields.io/github/stars/tensorwerk/hangar-py.svg?style=social) - Version control for tensor data, git-like semantics on numerical data with high speed and efficiency. * [Keepsake](https://github.com/replicate/keepsake) ![](https://img.shields.io/github/stars/replicate/keepsake.svg?style=social) - Version control for machine learning. +* [KitOps](https://github.com/jozu-ai/kitops) ![](https://img.shields.io/github/stars/jozu-ai/kitops.svg?style=social) - KitOps is an open and standards-based packaging and versioning system for AI/ML projects that works with all the AI/ML, development, and DevOps tools you are already using. * [lakeFS](https://github.com/treeverse/lakeFS) ![](https://img.shields.io/github/stars/treeverse/lakeFS.svg?style=social) - Repeatable, atomic and versioned data lake on top of object storage. * [MLflow](https://github.com/mlflow/mlflow) ![](https://img.shields.io/github/stars/mlflow/mlflow.svg?style=social) - Open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. * [ModelDB](https://github.com/VertaAI/modeldb) ![](https://img.shields.io/github/stars/VertaAI/modeldb.svg?style=social) - An open-source system to version machine learning models including their ingredients code, data, config, and environment and to track ML metadata across the model lifecycle.