This course provides an in-depth exploration of Machine Learning Operations (MLOps), focusing on the practices and tools essential for deploying, managing, and scaling machine learning models in production environments. Students will learn how to bridge the gap between data science and IT operations, covering topics such as version control, containerization, continuous integration and deployment (CI/CD), monitoring, and model lifecycle management. The curriculum emphasizes practical skills, enabling students to implement automated workflows for model training, validation, and deployment, as well as strategies for ensuring model performance and reliability post-deployment. By the end of the course, students will be proficient in MLOps methodologies and equipped with the skills to effectively manage machine learning projects from development through to production, ensuring that they are robust, scalable, and maintainable. The course includes projects or assignments focusing on practical use cases related to course topics, or hands-on learning aligned with globally recognized professional certifications related to the subject area