Courses

A comprehensive course on machine learning compilation, offering the first systematic treatment of key elements in this emerging field. Learn about ML programming abstractions, learning-driven search, compilation, and optimized library runtime. This course covers deploying innovative AI models across different production environments, addressing challenges in both training and inference workloads.

This tutorial explores comprehensive strategies for efficient serving of generative large language models, covering both algorithmic approaches and system-level optimizations. We present a survey from algorithms to systems, discussing key techniques for improving inference performance, reducing latency, and optimizing resource utilization across diverse hardware platforms.

This tutorial at SIGMOD 2024 focuses on efficient systems for large language model serving, presenting state-of-the-art techniques for optimizing LLM inference in database and data management contexts. We cover system design principles, optimization strategies, and practical deployment considerations for serving LLMs at scale.

This tutorial provides a comprehensive understanding of state-of-the-art techniques in the design and implementation of systems and compilers that optimize the performance of generative AI models, especially for large language models. We present Mirage, a multi-level superoptimization-based tensor program compiler, FlexFlow Serve for low-latency LLM serving, and FlexLLM for memory-efficient LLM finetuning.