Deployment & Production

О курсе

In this course, you will learn the essential skills required to take machine learning models from development to real-world production environments. You’ll discover how to package and optimize models for fast, reliable inference; build APIs for real-time serving; design scalable batch and streaming prediction systems; and monitor deployed models for performance, drift, and reliability. You will also explore A/B testing, safe rollout strategies, and model update workflows that ensure stability in high-stakes applications. By completing this course, you’ll gain the practical knowledge needed to bridge the gap between modeling and production—one of the most in-demand capabilities in modern ML engineering. You’ll learn not just how to deploy models, but how to keep them healthy, adaptive, and cost-efficient over time. What makes this course unique is its integration of conceptual understanding with hands-on production practices, guided by experts who have deployed models at scale. Whether you’re aiming to become an ML engineer, enhance your MLOps skills, or deepen your understanding of real-world deployment challenges, this course provides the tools and confidence needed to operate machine learning systems in production environments.

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