MLOps & Model Governance

О курсе

In this course, you will learn how to design, operate, and govern machine learning systems across their full lifecycle. You will gain the ability to track experiments, version models and data, build reproducible pipelines, implement CI/CD for ML, manage computational resources, and monitor deployed models for drift and degradation. You will also learn how to document models responsibly, create model cards, and apply governance practices that support ethical, compliant, and trustworthy AI. By completing this course, you will be prepared to move beyond experimentation and contribute to production-grade ML systems that are maintainable, auditable, and scalable. You will develop practical skills for communicating results to technical and non-technical stakeholders, aligning ML work with business and regulatory requirements, and ensuring models remain reliable over time. What makes this course unique is its integrated focus on both MLOps execution and model governance. Drawing on expertise from Coursera and DeepLearning.AI, the course combines hands-on pipeline construction with responsible AI practices, showing not only how to deploy models, but how to manage risk, accountability, and long-term impact. Whether you are an aspiring ML engineer or a data scientist transitioning to production systems, this course equips you with the skills needed to operate ML responsibly in real-world environments.

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