MLOps: CI/CD, Deployment, and Performance

О курсе

This course will help you in managing the full operational lifecycle of machine learning systems—from collaborative experimentation and version control to deployment, validation, and performance monitoring in production. You will learn how to apply standardized branching strategies, manage pull and merge request workflows, containerize models for reproducible environments, integrate automated data and model validation into CI pipelines, and analyze system performance using descriptive analytics. Completing this course equips you with the practical skills required to move machine learning projects beyond notebooks and into reliable, scalable production systems. You will gain hands-on experience with real-world MLOps practices used by industry teams to reduce risk, improve collaboration, and ensure model quality over time. These skills will help you work more effectively with engineers, data scientists, and stakeholders while building systems that are maintainable, auditable, and resilient to change. What makes this course unique is its end-to-end, practitioner-focused approach. Drawing on expertise from multiple industry and academic partners, the course connects version control, CI/CD, deployment, DataOps, and monitoring into a single coherent workflow. Rather than focusing on isolated tools, you’ll learn how MLOps practices fit together to support trustworthy, production-grade machine learning systems.

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