MLOps Foundations: Build, Deploy, and Monitor Production ML Pipelines

Transitioning a machine learning model from a local notebook to a reliable production environment requires more than just good code. This course introduces you to the core principles of Machine Learning Operations (MLOps), bridging the gap between data science and system engineering. You will transition from training isolated models to building automated, repeatable ML pipelines. By understanding how to manage code, data, and models systematically, you will gain the skills needed to ensure your machine learning systems remain accurate, scalable, and secure in production. What you'll learn: - Understand the foundational concepts of MLOps, model lifecycles, and the roles of data scientists and ML engineers. - Build automated machine learning pipelines to streamline data preparation, training, and evaluation. - Deploy models to cloud environments using scalable serving architectures and modern API endpoints. - Monitor production model performance, set up alerts, and detect data drift and concept drift over time. - Implement continuous integration and continuous delivery (CI/CD) practices specifically tailored for machine learning code and artifact tracking. - Configure continuous retraining strategies to keep models updated without manual intervention. The course begins with essential MLOps terminology and lifecycle definitions before guiding you through pipeline design, deployment strategies, and production monitoring. You will learn through clear, written explanations and practical code snippets designed for real-world application. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to operations and want to build a solid foundation in production ML systems. No prior DevOps or cloud administration experience is required. Start reading today to master the workflows that power modern production machine learning.