Optimizing and Operationalizing LLMs

This unit covers practical techniques to optimize inference performance and operationalize large language model solutions, including hardware utilization, batch tuning, acceleration tools, and observability. Students will also learn to balance performance and cost and assess emerging trends to plan maintainable GenAI deployments.

Learning Objectives

• Analyze hardware utilization and batch-tuning results to identify performance bottlenecks and recommend configuration changes based on profiling metrics (e.g., latency, throughput, GPU/TPU utilization).
• Apply inference-acceleration techniques and tools (for example, quantization, compilation, sharding, and runtime optimizers) to meet defined latency and throughput targets for a given deployment scenario.
• Demonstrate the implementation of monitoring and observability pipelines using logs, metrics, and traces to detect model degradation, trigger alerts, and support incident response.
• Evaluate performance-vs.-cost trade-offs and develop a deployment and scaling plan that balances SLA requirements, budget constraints, and anticipated future trends in GenAI applications.