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.
Applied Learning Project
Throughout this Specialization, you'll complete hands-on projects that mirror real-world MLOps challenges. You'll write Dockerfiles and deploy containerized models to Kubernetes clusters, build FastAPI-based prediction services with GitHub Actions CI/CD pipelines, and conduct load testing to meet SLA targets. You'll also construct automated ML pipelines using scikit-learn, implement test-driven data loaders and training loops, configure Horizontal Pod Autoscalers for resource optimization, and apply inference optimization techniques like quantization and pruning. These projects provide authentic experience managing the full ML production lifecycle.
