MLOps and LLMOps: Deploying and Scaling AI Models

Transitioning artificial intelligence from a local notebook to a reliable production system requires a specialized set of practices. If you want to understand how modern software engineering principles apply to machine learning and large language models, this foundational guide is your starting point. Through clear, written explanations and practical code examples, you will learn how to design, deploy, and monitor scalable AI systems. You will build a solid understanding of the entire lifecycle of both traditional machine learning models (MLOps) and modern large language models (LLMOps). What you'll learn: 1. Understand core concepts of model lifecycles, versioning, and registry management. 2. Configure continuous integration and continuous delivery (CI/CD) pipelines tailored for machine learning. 3. Deploy large language models using modern retrieval-augmented generation (RAG) architectures. 4. Monitor model performance, track data drift, and implement modern observability practices. 5. Apply scaling strategies to handle high-throughput inference efficiently. The course begins with foundational definitions of MLOps and LLMOps, establishing key terminology before guiding you through deployment pipelines, orchestration, and real-time monitoring. You will progress from basic model packaging to managing complex, production-ready AI workflows. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to operationalizing AI. No prior DevOps experience is required, though a basic familiarity with Python is helpful. Start reading today to bridge the gap between AI development and production-grade engineering.