Automated Model Deployment with GitHub Actions and MLOps

Transitioning a machine learning model from a local notebook to a reliable production environment is one of the most critical steps in the machine learning lifecycle. Automating this process ensures your models are tested, packaged, and deployed consistently without manual intervention. This text-based course guides you through the core concepts of Machine Learning Operations (MLOps) and continuous delivery. You will understand how to write automated workflows that trigger on code changes, package your model into a container, and deploy it securely to an online endpoint. What you'll learn: - Understand foundational MLOps principles and the model deployment lifecycle - Configure GitHub Actions workflows to automate testing and deployment steps - Build container images to package your machine learning models and dependencies - Deploy trained models to online endpoints for real-time inference - Implement basic testing for model inputs and deployment configurations - Manage secrets and credentials securely within your automation pipelines You will start with key terminology and foundational concepts of CI/CD and MLOps before moving into practical configuration. Through written explanations and clear code snippets, you will learn how to write workflow files, containerize models, and verify successful deployments. This course is designed for beginner data scientists, aspiring MLOps engineers, and software developers new to machine learning deployment, requiring no prior DevOps experience. Start automating your machine learning workflows today.