MLOps Fundamentals: Deploying and Monitoring Machine Learning Models

Moving a machine learning model from a local notebook to a reliable production environment requires a specialized set of skills. This text-based course introduces you to the core principles of Machine Learning Operations (MLOps), bridging the gap between data science and software engineering. By the end of this course, you will understand how to automate model pipelines, manage data versioning, and set up continuous integration and continuous deployment (CI/CD) for your AI systems. What you'll learn: - Understand foundational MLOps concepts, terminology, and the machine learning lifecycle. - Configure automated pipelines for data preprocessing, model training, and evaluation. - Apply version control to both code and datasets to ensure reproducibility. - Deploy models to production using containerization basics and cloud services. - Monitor model performance in real-time to detect data drift and maintain accuracy. - Implement basic CI/CD workflows tailored for machine learning pipelines. The course begins with essential terminology and structural foundations before guiding you through step-by-step written explanations of pipeline design, deployment strategies, and ongoing model observability. This course is designed for aspiring machine learning engineers, data scientists, and software developers who are new to operations and want to learn how to manage production models, with no prior DevOps experience required. Start reading today to build reliable, automated, and scalable machine learning systems.