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Comprehensive tutorial on using GitHub Actions and Continuous Machine Learning (CML) to automate machine learning workflows! In this video, we’ll walk through the complete process of setting up a CI/CD pipeline for a machine learning project focused on churn prediction. By the end, you’ll be able to create and deploy automated workflows, monitor model performance, and collaborate seamlessly with your team! By setting up CI/CD for your ML projects, you can: Automate model training every time you make changes to your code. Test and validate models continuously to ensure performance stays consistent. Deploy models seamlessly, whenever they're ready. ⭐️ Contents ⭐️ 0:00 Introduction to CI/CD Concepts for ML 1:00 GitHub Actions and Continuous Machine Learning (CML) workflow 05:43 GitHub Repository Setup 08:19 Dataset Preparation 10:06 Project Codebase: Building the Machine Learning Model Pipeline 39:33 Testing Churn Prediction 40:18 Next-Step 🔗 Links & Resources Part 2 - • Part 2 | MLOps On GitHub | Deploy and Auto... Article - / automate-ml-and-llm-workflow-with-github-a... Code & Project Files: https://github.com/Abonia1/Github-Act... GitHub Actions Documentation: https://docs.github.com/actions CML Documentation: https://cml.dev/doc Github Token to authenticate GitHub action: https://docs.github.com/en/actions/se... ___________________________________________________________________________ 🔔 Get our Newsletter and Featured Articles: https://abonia1.github.io/newsletter/ 🔗 Linkedin: / aboniasojasingarayar 🔗 Find me on Github: https://github.com/Abonia1 🔗 Medium Articles: / abonia #GitHubActions, #CML, #MachineLearning, #MLOps, #DataScience, #CICD, #Automation, #MLPipeline, #AI, #Scikitpipeline, #MLModel#DataScience