Production Machine Learning: Designing and Deploying ML Systems

Building a machine learning model is only the first step; the real challenge lies in deploying, scaling, and maintaining it in a live production environment. This text-based course guides you through the entire machine learning lifecycle, teaching you how to design robust systems that deliver continuous value. You will learn how to transition from writing isolated experimental code to building automated pipelines that handle data drift, model deployment, and real-time monitoring. What you'll learn: 1. Understand the core terminology and phases of the production machine learning lifecycle. 2. Design data ingestion and preprocessing pipelines that ensure consistent data quality. 3. Configure model deployment strategies, including API creation and containerization basics. 4. Implement continuous monitoring to detect model degradation and data drift over time. 5. Apply modern CI/CD principles to automate model retraining and testing. 6. Explore foundational MLOps tools and patterns for scalable infrastructure. You will start with the fundamental concepts of system design before exploring data management, model deployment, and post-deployment monitoring. Through clear explanations and practical code examples, you will learn how to keep your production systems running smoothly. This course is designed for software engineers, data scientists, and aspiring MLOps professionals who understand basic machine learning concepts and want to learn production best practices. No prior production deployment experience is required. Start building resilient, production-ready machine learning systems today.