MLOpsPython

Why we included this project

Teams that need to stand up repeatable machine learning delivery pipelines will find a concrete, working reference here rather than another abstract architecture diagram. This repository ships a full template for an ML project wired through Azure DevOps and Azure Machine Learning, with continuous integration, continuous delivery, and scheduled model retraining all expressed as executable code and pipeline definitions. It walks through the steps that often trip teams up in production: data sanity checks and unit tests before training, training on different compute targets, registering and versioning models, evaluating candidates before selection, and deploying a real-time web service through staged QA and production releases. Because it is built around the scikit-learn diabetes example while keeping the scaffolding separate from the model logic, the same structure can be adapted to other scenarios and build systems such as Jenkins or Travis. If you want a concrete map of what a mature CI/CD flow for ML looks like on Azure, this is a useful place to start.