Deploying and Maintaining Production AI Systems

Many AI projects stall because moving a model from a local notebook to a live environment introduces complex operational challenges. This text-based course helps you bridge the gap between building models and running them reliably in production. You will understand how to build robust deployment pipelines, monitor model performance over time, and handle real-world challenges like data drift and system degradation. What you'll learn: Understand the fundamental concepts of MLOps and the lifecycle of production AI; Deploy machine learning models as scalable, accessible web services; Monitor model performance and detect data drift using modern observability practices; Configure automated deployment pipelines and basic CI/CD workflows for AI; Maintain system reliability through robust error handling and logging strategies. The course starts with foundational definitions and key deployment terminology before moving into step-by-step written explanations of deployment patterns, monitoring, and long-term maintenance strategies. This course is designed for beginner developers, data scientists, and engineers who are new to MLOps, with no advanced infrastructure prerequisites required. Start reading today to build and maintain resilient AI systems in production.