Operationalizing Large Language Models on Azure

Scaling large language models from a simple experimental prompt to a production-ready application requires a robust and managed infrastructure. This course provides a structured path to understanding how to deploy, manage, and monitor generative AI models within the Azure ecosystem. You will learn to navigate AI services and implement the operational workflows necessary to maintain reliable AI-driven systems. You will transform your understanding of AI from basic experimentation into a professional operational framework. By following written technical explanations and architectural walkthroughs, you will gain the skills needed to support modern AI workloads at scale. What you'll learn: - Understand the core architecture and terminology of large language models. - Configure Azure environments to host and manage generative AI workloads. - Apply prompt engineering techniques to improve model accuracy and relevance. - Implement Retrieval-Augmented Generation (RAG) patterns using vector databases. - Establish monitoring and governance frameworks for secure AI operations. - Practice deploying and scaling models through the Azure portal. The course begins with essential definitions and a look at the current AI landscape before progressing into deployment strategies and modern architectural patterns. This program is designed for beginners and professionals with foundational programming knowledge who want to gain practical skills in AI infrastructure. Start building your production-grade AI environment today.