The success of an ML model today still depends on the hardware it runs on, which makes it important for people working with ML models in production to understand how their models are compiled and optimized to run on different hardware accelerators. This talk starts with the benefits of bringing ML to edge devices, which motivates the need for optimizing compilers for ML models. It continues to discuss how compilers work, and ends with an exciting new direction: using ML to power compilers that can speed up ML models!

Chip Huyen, is an engineer and founder working on infrastructure for real-time machine learning. Through her work with Snorkel AI, NVIDIA, and Netflix, she has helped some of the world’s largest organizations deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s also published four bestselling Vietnamese books.

свернуть