In this course you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps.

This course is designed for data scientists, machine learning engineers, AI practitioners, and IT professionals who want to operationalize machine learning workflows, scale AI systems, and streamline deployment and infrastructure management. To get the most out of this course, learners should have a basic understanding of machine learning concepts, be familiar with Python programming, and have experience using Docker and containerization technologies. By the end of this course, learners will be able to operationalize machine learning models by designing scalable MLOps workflows, automating deployments with CI/CD pipelines, monitoring performance and detecting data drift, and optimizing AI infrastructure using tools like Docker, MLflow, and Kubernetes to support robust, real-world AI applications.