Deploying and Optimizing LLM Inference at Scale

Deploying AI models, especially large language models, presents unique challenges when aiming for high performance and efficiency in production. This course provides the foundational knowledge to successfully manage the complexities of AI inference in real-world environments. By the end of this course, you will be equipped to architect and implement robust, optimized inference pipelines for demanding AI applications, transforming theoretical understanding into practical deployment skills. What you'll learn: Understand the core concepts of AI model inference, its lifecycle, and performance metrics. Learn strategies for optimizing model performance and resource usage, including quantization and pruning techniques. Apply containerization and orchestration principles to build scalable and resilient inference services. Configure monitoring and observability tools to track the health and performance of deployed AI inference systems. Design efficient and fault-tolerant inference architectures specifically tailored for large language models. Practice deploying and scaling inference services through guided, text-based exercises. The course begins by establishing core principles of AI model deployment, then progresses through practical optimization techniques and modern infrastructure patterns for achieving high-throughput, low-latency inference. You'll gain a step-by-step understanding of moving models from development to scalable production. This course is designed for beginners in AI engineering, MLOps, or software development who want to learn how to deploy and manage AI models at scale. No prior experience with large-scale AI deployment or specific infrastructure knowledge is required. Start building your expertise in scalable AI inference today.