Show HN: Continuous Machine Learning – CI/CD for Machine Learning Projects
Hi, I'm one of the project creators. Continuous Machine Learning (CML) is an open source project to help ML projects use CI/CD with Github Actions and Gitlab CI (<a href="https://github.com/iterative/cml" rel="nofollow">https://github.com/iterative/cml</a>).<p>CML automatically generates human-readable reports with metrics and data viz in every pull/merge request, and helps you use storage and GPU/CPU resources from cloud services. CML addresses three hurdles for making ML compatible with CI:<p>1. In ML, pass/fail tests aren’t enough. Understanding model performance might require data visualizations and detailed metric reports. CML automatically generates custom reports after every CI run with visual elements like tables and graphs. You can even get a  Tensorboard.dev link as part of your report.<p>2. Dataset changes need to trigger feedback just like source code. CML works with DVC so dataset changes trigger automatic training and testing.<p>3.Hardware for ML is an ecosystem in itself. We’ve developed use cases with CML and Docker Machine to automatically provision and deploy cloud compute instances (CPU & GPU) for model training.<p>Our philosophy is that ML projects- and MLOps practices- should be built on top of traditional software tools and CI systems, and not as a separate platform. Our goal is to extend DevOps’ wins from software development to ML. Check out our project site (<a href="https://cml.dev" rel="nofollow">https://cml.dev</a>) and repo, and please let us know what you think!