LLM Deployment and LLMOps: Scaling Models in Production

Deploying large language models into production requires more than just API calls; it demands robust operations, cost optimization, and scalable infrastructure. This text-based course guides you through the core principles of LLMOps to transition your models from development to reliable production environments. You will gain a deep understanding of how to manage the lifecycle of models like Llama, optimize inference speed, and minimize computational costs. By studying practical architectures and configuration patterns, you will learn to build efficient, scalable, and secure AI deployment pipelines. What you'll learn: - Understand the foundational concepts of LLMOps, model lifecycles, and the transition from traditional MLOps to LLM-specific pipelines. - Configure and track models using MLflow for versioning, logging, and systematic lifecycle management. - Apply advanced optimization and quantization techniques, including GPTQ, AWQ, and LoRA, to reduce model size and running costs. - Scale inference workloads efficiently using Ray, batching strategies, Flash Attention, and Paged Attention. - Integrate modern retrieval-augmented generation (RAG) patterns and observability frameworks to monitor model performance and trace outputs. Starting with foundational definitions of model hosting, the course guides you step-by-step through configuration, optimization, scaling, and production monitoring. You will learn through clear written explanations, structured architectural walkthroughs, and conceptual exercises. This course is designed for software engineers, data scientists, and aspiring AI engineers who are new to model deployment and want to build a solid foundation in LLMOps. No prior experience with production scale-out is required. Begin your journey into production-grade AI engineering and start optimizing your deployments today.