A GitHub template for automating machine learning workflows on Azure using GitHub Actions.
ML Template Azure is a GitHub repository template that helps data scientists and machine learning engineers set up automated MLOps pipelines on Azure Machine Learning. It provides a pre-configured structure and GitHub Actions workflow to automate training, model registration, and deployment of ML models. The template solves the problem of manually managing ML workflows by enabling CI/CD for machine learning projects.
Data scientists and ML engineers working on Azure who want to implement automated ML pipelines using GitHub Actions. It's ideal for teams looking to adopt MLOps practices without building infrastructure from scratch.
Developers choose this template because it provides a production-ready starting point with certified Azure ML GitHub Actions, reducing setup time and ensuring best practices. The main advantage is having an automated pipeline that triggers on code changes, allowing teams to focus on model development rather than infrastructure.
Template for getting started with automated ML Ops on Azure Machine Learning
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Includes a pre-configured GitHub Actions workflow (train_deploy.yml) that triggers on code changes, automating training, model registration, and deployment as documented in the sample.
Uses certified GitHub Actions like aml-workspace and aml-deploy for reliable interaction with Azure Machine Learning, streamlining workspace management and deployments.
Provides clear folders for training scripts, environment configs, and deployment code, as shown in the code structure table, making it easy to follow MLOps best practices.
Step-by-step instructions for setting up service principal authentication with Azure CLI and GitHub secrets, enhancing security for automated workflows.
Tightly coupled with Azure Machine Learning and GitHub Actions, making migration to other clouds or CI/CD tools difficult and limiting flexibility for diverse environments.
Requires manual setup of Azure service principal and GitHub secrets, which can be error-prone, as indicated by the detailed steps and known issues like MissingSubscriptionRegistration.
The template is opinionated towards a specific workflow; teams with unique pipeline requirements may need extensive modifications to the provided actions and scripts.