LLMOps and Generative AI: Deploying Production Models

As Large Language Models become central to modern software, the ability to move from a simple prompt to a reliable production application is a critical skill. Understanding how to manage, deploy, and scale these models is what separates a prototype from a professional-grade AI solution. You will transition from understanding basic AI concepts to implementing robust LLMOps workflows that ensure your generative applications are scalable, maintainable, and efficient. By focusing on the operational side of artificial intelligence, you will learn how to bridge the gap between experimental code and production-ready systems. What you'll learn: - Understand the fundamental differences between discriminative and generative models. - Apply advanced prompt engineering strategies to improve model output quality and reliability. - Implement Retrieval-Augmented Generation (RAG) to connect models with external data sources and vector databases. - Configure automated evaluation frameworks to measure the accuracy and safety of LLM applications. - Deploy generative models to production environments using Hugging Face and OpenAI interfaces. - Manage the operational lifecycle of AI systems with modern observability and monitoring practices. The course begins with foundational definitions of LLMs and MLOps before moving into practical implementation patterns. You will read through detailed architectural explanations and study code snippets that demonstrate how to package, serve, and monitor models effectively in real-world scenarios. This course is designed for beginners interested in the intersection of AI and operations; no prior experience with machine learning deployment or high-level data science is required. Start building your foundation in the operational side of Generative AI today.