Foundations of MLOps: DevOps Principles for Machine Learning

Traditional software development has established reliable pipelines, but deploying machine learning models introduces unique challenges in data, code, and model versioning. This text-based course guides you through the foundational principles of MLOps, showing you how to bring the discipline of DevOps to the world of artificial intelligence. You will transition from manual model training to designing automated, reproducible ML workflows. By understanding the intersection of data engineering, model development, and operational deployment, you will learn how to maintain robust machine learning systems in production environments. What you'll learn: Understand the core principles of MLOps and how they differ from traditional DevOps; Configure automated pipelines for data validation, model training, and evaluation; Apply containerization basics to package machine learning models for consistent deployment; Implement version control for both code and datasets to ensure reproducible experiments; Establish modern observability and monitoring practices to detect model drift in production; Explore deployment patterns for advanced applications, including RAG-based systems. This course begins with essential terminology and foundational MLOps concepts before guiding you through step-by-step written explanations of continuous integration, automated deployment, and system monitoring. You will read real-world scenarios and practice with structured code snippets to solidify your understanding. This course is designed for beginner data scientists, software engineers, and system administrators looking to enter the field of MLOps. No prior DevOps experience is required, though a basic understanding of machine learning concepts is helpful. Start reading today to master the operational side of modern machine learning.