MLOps, short for Machine Learning Operations, is a set of practices that have been developed to assist in the deployment and maintenance of machine learning models in a production environment. In both industry and research, MLOps has gained significant attention as a means to ensure that machine learning systems generate value. Our course, Discover Full Automation in MLOps, focuses on teaching you how to utilize automation in MLOps to deploy machine learning systems that can consistently deliver value over time. Throughout the course, you will gain an understanding of the impact of hidden technical debt on machine learning systems and the value they produce. Additionally, you will learn how automating and streamlining the various stages of the machine learning lifecycle can greatly enhance the operation and scalability of these systems. Through a combination of hands-on exercises and interactive learning experiences, you will become familiar with the components of an MLOps architecture and how they are essential for achieving full automation in machine learning systems. Furthermore, the course will delve into the techniques of Continuous Integration/Continuous Deployment (CI/CD), Continuous Monitoring (CM), and Continuous Training (CT) in the context of MLOps. These techniques are crucial for avoiding technical debt and ensuring the success of your machine learning deployments. By the conclusion of the course, you will have a comprehensive understanding of how automation, when implemented through MLOps, can significantly enhance the deployment of your machine learning systems in real-world scenarios. This will result in deployments that are robust, scalable, and capable of generating consistent value. Embark on this learning journey to gain valuable knowledge in this highly sought-after field and discover how to effectively apply automation when designing MLOps systems.