Model Serving and MLOps: Deploying Machine Learning to Production

Transitioning a machine learning model from a local notebook to a reliable production environment requires a specific set of engineering skills. This text-based course guides you through the core principles of MLOps, helping you bridge the gap between data science and software engineering. You will learn how to transition from training models to serving them reliably to real users. By understanding the lifecycle of production ML systems, you will be able to design robust deployment pipelines, monitor model performance, and handle real-world data drift. What you'll learn: Understand foundational MLOps concepts, lifecycle stages, and the difference between development and production environments; Configure model serving architectures to handle real-time and batch predictions; Implement drift detection strategies to identify when models need retraining; Apply basic containerization concepts to package models consistently; Establish simple continuous integration workflows and observability metrics for model health. The course begins with essential terminology and the MLOps lifecycle before moving into deployment strategies, containerization, and post-deployment monitoring. You will learn through clear, text-based explanations and practical configuration examples. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to MLOps. No prior production deployment experience is required, though a basic understanding of machine learning concepts is helpful. Start your journey into production-grade machine learning engineering today.