Part 1 | MLOps On GitHub | Deploy and Automate ML Workflow |Using GitHub Actions and CML for CI& CD

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 - https://youtu.be/u_rCPdZY2g4 - Article - https://medium.com/@abonia/automate-ml-and-llm-workflow-with-github-actions-cml-1673c9544c3c - Code & Project Files: https://github.com/Abonia1/Github-Action-for-ML - 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/security-for-github-actions/security-guides/automatic-token-authentication#permissions-for-the-github_token ___________________________________________________________________________ 🔔 Get our Newsletter and Featured Articles: https://abonia1.github.io/newsletter/ 🔗 Linkedin: https://www.linkedin.com/in/aboniasojasingarayar/ 🔗 Find me on Github: https://github.com/Abonia1 🔗 Medium Articles: https://medium.com/@abonia #GitHubActions, #CML, #MachineLearning, #MLOps, #DataScience, #CICD, #Automation, #MLPipeline, #AI, #Scikitpipeline, #MLModel#DataScience