ML Production Systems

About this course

This Specialization equips you with the end-to-end skills needed to move machine learning models from development into robust production systems. You'll learn to containerize and deploy ML models using Docker and Kubernetes, build RESTful inference services with CI/CD automation, optimize hyperparameters systematically, and construct automated scikit-learn pipelines. The program also covers test-driven development practices for reliable ML code, advanced Kubernetes resource optimization for scalable infrastructure, and Git-based workflows for managing production codebases. Through hands-on projects and practical exercises, you'll gain the MLOps expertise that modern AI teams demand—bridging the gap between data science experimentation and production engineering to deliver ML systems that are reliable, scalable, and maintainable.