MLOps and LLMOps: Operationalizing Machine Learning and Generative AI

Taking a machine learning model or generative AI application from a local notebook to a reliable production environment requires a specialized set of practices. This text-based course guides you through the essential principles of MLOps and LLMOps to successfully operationalize your AI solutions. You will transition from writing raw model code to designing robust deployment pipelines, managing model registries, and setting up continuous monitoring. By studying clear written explanations and analyzing practical configuration examples, you will understand how to maintain system reliability, manage API costs, and ensure consistent AI performance. What you'll learn: Understand the core lifecycle of machine learning and generative AI operations (MLOps and LLMOps); Deploy predictive models and large language models to secure production endpoints; Implement Retrieval-Augmented Generation (RAG) patterns and manage vector databases; Configure automated pipelines for model registration, versioning, and continuous integration; Monitor production AI systems for data drift, model degradation, and latency; Apply modern observability practices to track generative AI token usage and API costs. The course begins with foundational definitions of AI operationalization before moving step-by-step through deployment strategies, pipeline automation, and production monitoring. Designed for beginner developers, data scientists, and system administrators looking to enter the field of AI operations, this course requires no prior DevOps experience. Start reading today to bridge the gap between AI development and production-ready operations.