Gain a comprehensive understanding of Machine Learning Operations (MLOps) in this professional course. Designed for data scientists, engineers, and leaders, this course will equip you with the knowledge and skills necessary to seamlessly transition machine learning models from local notebooks to production-ready systems. Throughout the course, you will delve into the fundamental concepts of MLOps, exploring the different phases involved in MLOps processes and gaining insight into the varying levels of MLOps maturity. Armed with this understanding, you will be well-prepared to implement machine learning continuously, reliably, and efficiently. Additionally, this course will address the challenges of scaling and automating machine learning projects. You will learn how to optimize time and resource allocation, as well as automate processes to minimize manual intervention and enhance model performance. By exploring the core features and associated roles of MLOps, you will develop a solid foundation in this field. Furthermore, you will gain practical knowledge of systems and tools that facilitate the scaling and automation of machine learning operations. Topics covered include feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. By familiarizing yourself with these key MLOps concepts, you will be equipped to apply them effectively in real-world scenarios.