Foundations of MLOps: CI/CD, APIs, and Machine Learning Deployment

Transitioning a machine learning model from a local notebook to a reliable production environment requires a specialized set of software engineering practices. This text-based course guides you through the core concepts of MLOps, bridging the gap between data science and operational DevOps. You will learn how to automate the lifecycle of machine learning models, from version control and continuous integration to deploying scalable APIs and setting up basic monitoring systems. What you'll learn: - Understand foundational MLOps principles and the machine learning lifecycle. - Build clean, high-performance APIs for model serving using modern frameworks like FastAPI. - Configure CI/CD pipelines to automate model testing, validation, and delivery. - Containerize machine learning applications using Docker for consistent deployments. - Implement basic model monitoring and drift detection strategies in production. The course starts with essential terminology and foundational definitions before moving step-by-step through environment configuration, API design, automated testing, and deployment strategies. This training is designed for beginner developers, data scientists, and aspiring MLOps engineers looking to understand deployment workflows without needing prior DevOps experience. Start building production-ready machine learning pipelines today.