Production Machine Learning and MLOps Fundamentals

Building a machine learning model in a sandbox environment is only the first step; the real challenge lies in deploying, scaling, and maintaining it in a live production environment. This text-based course bridges the gap between theoretical data science and practical machine learning engineering. You will transition from writing experimental code to designing end-to-end production pipelines. You will gain a clear understanding of how to scope projects, establish reliable baselines, manage data pipelines, deploy models, and implement continuous monitoring to handle real-world challenges like concept drift. What you'll learn: - Understand the core phases of the production machine learning lifecycle, from project scoping to system design. - Establish baseline performance metrics and conduct structured error analysis to guide model iteration. - Design robust data pipelines and address data quality issues, including handling concept and data drift. - Configure deployment strategies and select appropriate architectures for real-time and batch prediction. - Monitor live model performance using modern observability concepts and automated feedback loops. The course begins with foundational definitions of production systems and MLOps principles before guiding you through data preparation, deployment patterns, and post-deployment maintenance. Through written explanations and practical code scenarios, you will learn to think like a machine learning engineer. This course is designed for aspiring ML engineers, data scientists, and developers who are new to production workflows. No prior MLOps experience is required, as we start with the absolute basics of the production lifecycle. Start your journey toward building reliable, production-ready machine learning systems today.