Optimizing and Deploying LLM Systems

This course advances your skills from building working LLM prototypes to scaling, integrating, and deploying production-grade AI systems. You’ll blend system-level concepts with hands-on engineering to profile performance, integrate real-time data and multimodal sources, and ship secure, cloud-deployed applications. Whether you’re a developer, data scientist, or AI practitioner, this course gives you a clear roadmap to transform optimized LangChain workflows into reliable, observable services that interact with live APIs, structured data, and orchestration frameworks. Through guided lessons, structured demonstrations, and project-based learning, you’ll learn how to profile latency and token usage, design efficient prompts and chains, and evaluate pipelines with LLMOps metrics. You’ll connect external APIs, build hybrid retrieval across text, tables, and images, and orchestrate complex data flows using LlamaIndex and LangGraph. Finally, you’ll containerize and deploy a FastAPI service with authentication, monitoring, and CI/CD, culminating in an end-to-end capstone deployment. By the end of this course, you will be able to: • Profile and optimize LLM pipelines for latency, throughput, and token/cost efficiency. • Design prompt and chain strategies (dynamic templates, caching, auto-tuning) to improve reliability and speed. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Integrate real-world data via secure APIs and hybrid retrieval across structured, unstructured, and multimodal sources. • Orchestrate data and evaluation workflows using LlamaIndex and LangGraph for scalable reasoning. • Build, secure, containerize, and deploy a FastAPI service with JWT/OAuth, monitoring, and CI/CD automation. This course is ideal for AI developers, data scientists, and software engineers ready to move beyond prompt experimentation and deliver production-ready LLM applications. A working knowledge of Python and APIs is recommended; all