GitHub Actions is a powerful feature of the GitHub platform that allows for automating software development workflows, enabling developers to streamline their development process. In this tutorial, we demonstrate how to use GitHub Actions for a beginner Machine Learning (ML) project, and cover everything from setting up our ML project on GitHub to creating a workflow that automates ML tasks.
GitHub Actions is a tool that provides a continuous integration and delivery (CI/CD) pipeline for all GitHub repositories. It automates the entire software development workflow from creating, testing, to deploying code, all within the GitHub platform. This tutorial uses two key Actions: actions/checkout@v3 and iterative/setup-cml@v2.
At the heart of GitHub Actions are workflows, which are automated processes that you define in your GitHub repository. They can be triggered by various GitHub events and are defined in a .github/workflows directory. Within workflows, jobs define a set of steps that execute on the same runner and can include commands or actions – reusable pieces of code that can perform a specific task. Runners are the virtual environments where workflows are executed, and can be host-owned by GitHub or self-hosted by the user.
This tutorial walks through a simple ML project using the Bank Churn dataset from Kaggle to train and evaluate a Random Forest Classifier, outlining how to set up the GitHub repository, clone and edit the repository, create necessary files, and write the code that will train, evaluate, and save the model pipelines. It ends with an implementation of a machine learning workflow for training and evaluating the model using continuous machine learning (CML) actions.
CML actions are implemented to automate the process of generating a model evaluation report. This means that any time changes are pushed to GitHub, a report is automatically generated under the commit and you will receive an email with this report.
In conclusion, the tutorial provides a step-by-step guide to using GitHub Actions for ML beginners, teaching how to streamline and automate ML tasks on GitHub. This is an important part of the MLOps (Machine Learning Operations) field. The tutorial ends with links to the complete code source, further tutorials for MLOps, and a recommendation of a course for becoming an MLOps engineer. The author of the tutorial, Abid Ali Awan, is a certified data scientist professional who is passionate about machine learning and AI.
