MLOps Engineering Fundamentals: Associate Exam and Career Prep

Bridging the gap between data science and production software engineering is one of the most critical challenges in modern technology. This text-based course guides you through the foundational concepts and workflows required to manage the entire lifecycle of machine learning models. You will transition from writing local experimental scripts to designing robust, automated MLOps pipelines that ensure model reliability, reproducibility, and continuous delivery in production environments. What you'll learn: - Understand the core principles of MLOps, including version control for data, code, and model artifacts - Build automated training and deployment pipelines using modern CI/CD patterns - Track experiments and manage model registries with tools like MLflow - Monitor model performance in production to detect data drift and concept drift - Implement foundational LLMOps practices for managing generative AI and large language models - Prepare for professional MLOps associate exams with comprehensive conceptual walkthroughs The course starts with essential definitions and terminology before moving into pipeline architecture, model deployment strategies, and real-world monitoring practices. You will read structured explanations, analyze architecture patterns, and study practical configuration examples. Designed for aspiring MLOps engineers, data scientists, and software developers looking to specialize in machine learning operations, this course requires no advanced MLOps experience, though basic familiarity with Python is recommended. Begin your journey toward mastering production-ready machine learning operations today.