Foundations of MLOps Engineering: Build Your Career in ML Operations

As machine learning models move from research to production, organizations need skilled professionals to bridge the gap between data science and software engineering. This text-only course provides a clear, step-by-step pathway to understanding the MLOps landscape and building the foundational skills required for this rewarding career. You will transition from a general tech background to understanding how to automate, monitor, and scale machine learning pipelines in production environments. What you'll learn: Understand core MLOps principles, career pathways, and the essential skills required in the industry today; Design automated machine learning pipelines for continuous integration and continuous delivery (CI/CD); Monitor deployed models in production to detect data drift and maintain performance over time; Explore modern LLMOps concepts, including deploying and managing large language model pipelines; Configure basic containerization and automation workflows tailored for machine learning. The course begins with foundational definitions and industry terminology before guiding you through pipeline automation, model monitoring, and practical deployment strategies. Designed for software engineers, data analysts, DevOps beginners, or anyone looking to break into the field of machine learning operations with no prior MLOps experience required. Start your journey toward becoming a proficient MLOps engineer today.