MLExray - Observability for Machine Learning on the Edge

Explore common challenges in deploying machine learning models on edge devices and learn how to address performance drops using MLExray, an open-source observability framework developed at Stanford. Discover why models that perform well in cloud environments often struggle when deployed across different edge environments. Gain insights into debugging techniques for machine learning deployments on the edge, and understand how MLExray can help maintain model accuracy in diverse real-world scenarios. Delve into the research behind MLExray, which has been accepted into MLSys 2022, and learn how this tool can bridge the gap between cloud performance and edge deployment realities.

MLExray: Observability for Machine Learning on the Edge - Michelle Nguyen, Stanford