Cloud MLOps: Deploying and Managing Machine Learning in Production

Moving a machine learning model from a local notebook to a reliable production environment requires specialized tools and practices. This text-only course introduces you to the core principles of Machine Learning Operations (MLOps) on modern cloud platforms. You will gain the skills to transition from manual model training to automated, observable machine learning pipelines. By reading through clear explanations and structured written exercises, you will understand how to package, deploy, monitor, and maintain models efficiently. What you'll learn: - Understand the core concepts of MLOps and the lifecycle of production machine learning systems. - Configure automated training and deployment pipelines using Vertex AI and cloud services. - Apply CI/CD practices specifically tailored for machine learning code, data, and models. - Implement model monitoring and observability to detect drift and performance degradation. - Manage datasets, features, and artifacts using centralized cloud repositories. - Practice deploying models as scalable APIs for real-time and batch predictions. The course begins with foundational MLOps terminology and architecture patterns, ensuring you understand the theory before moving on to practical pipeline design. You will then explore step-by-step written walkthroughs covering model deployment, continuous integration, and long-term maintenance in the cloud. This course is designed for aspiring data scientists, software engineers, and cloud beginners who want to learn MLOps principles from scratch. No prior cloud engineering experience is required, though a basic understanding of Python and machine learning concepts is helpful. Start reading today to build reliable, automated, and production-ready machine learning workflows in the cloud.