How Comet can streamline machine learning on The GitLab DevOps Platform

This post demonstrates integrating Comet ML with GitLab CI/CD to streamline the ML development workflow. It outlines a process for separating ML model training and evaluation code from application code into distinct GitLab projects. The CI/CD pipeline automates model training, evaluation, and reporting within merge requests, allowing for collaborative code and model reviews. Upon approval, the model is registered in Comet's Model Registry, and a downstream pipeline is triggered for deploying the ML-powered application's UI. Key technical details include the use of Python scripts for training, DVC for reporting, Comet's Model Registry for versioning, and manual trigger for deployment to Kubernetes.