MLOps Foundations: Deploying Machine Learning Models to Production

Moving a machine learning model from a local development environment to a reliable production system is one of the most critical challenges in modern software engineering. This text-based course guides you through the entire lifecycle of model deployment and production operations. You will learn how to transition from training models to building robust, automated pipelines that serve predictions reliably at scale. What you'll learn: Understand foundational MLOps principles, core terminology, and the model deployment lifecycle; Package machine learning models using containerization tools for consistent deployment; Configure automated CI/CD pipelines to streamline model testing and integration; Deploy models as APIs to serve real-time and batch predictions; Implement modern observability and monitoring to track model drift and performance degradation; Apply scaling and orchestration strategies to handle production traffic efficiently. Starting with core definitions and architectural patterns, the course progresses through hands-on deployment scenarios, configuration strategies, and post-deployment monitoring practices. This course is designed for beginner data scientists, software developers, and aspiring MLOps professionals with no prior production deployment experience. Start reading today to master the essential operational skills needed to run machine learning in production.