"DevOps Foundations for ML is designed for aspiring MLOps engineers, data scientists, and developers who want to bring DevOps discipline into machine learning workflows. You'll learn to automate, test, containerize, and deploy ML models using Git, GitHub Actions, Docker, and FastAPI — building production-ready pipelines end to end.

The first module builds your foundation in version control and automation. You'll configure Git repos, adopt branching strategies, and use GitHub Actions to automate testing and linting of ML code. The second module focuses on ML pipeline automation. You'll design multi-stage CI/CD workflows that handle data preprocessing, training, evaluation, and automated retraining with secure secret management. The third module teaches you to serve ML models as real-time REST APIs using FastAPI, covering input validation, latency optimization, testing, and OpenAPI documentation. The final module covers packaging and deployment. You'll containerize ML services with Docker, optimize image size, and automate deployments to cloud runners with monitoring. By the end of this course, you will: - Build CI/CD pipelines with GitHub Actions for automated ML testing and retraining - Develop and test ML REST APIs using FastAPI with validation and OpenAPI docs - Containerize ML services with Docker and deploy them to production - Apply version control and automated testing best practices for reproducible ML"