Generative AI Operations and Practice with Azure AI Studio

As generative AI moves from experimental prototypes to production systems, understanding how to manage and deploy these models is a vital skill. This course guides you through the foundational concepts and practical workflows needed to operate large language models effectively. You will transition from a basic understanding of AI to confidently managing generative models in professional environments. By reading through clear explanations, following structured code examples, and completing written analysis exercises, you will master the lifecycle of AI application development, from initial prompt engineering to model evaluation and deployment. What you'll learn: - Understand the core terminology of large language models, tokenization, and neural network foundations - Configure and navigate Azure AI Studio to deploy and test generative models - Apply advanced prompt engineering techniques and manage system instructions - Implement retrieval-augmented generation (RAG) patterns using vector databases for contextual accuracy - Monitor, evaluate, and optimize model performance using modern LLMOps practices - Address security, privacy, and content safety filtering in production AI systems The course begins with foundational definitions of generative models before moving into configuration within Azure AI Studio. You will then progress to advanced integration patterns, learning how to connect models to external data sources and establish robust operational workflows. This course is designed for aspiring AI engineers, developers, and technology professionals who want to understand the operational side of generative AI. No prior experience with machine learning or cloud architecture is required. Start reading today to build and manage production-ready generative AI systems.