Bridge Data Center AI Systems with Edge Computing for Actionable Information Retrieval

by   Zhengchun Liu, et al.

Extremely high data rates at modern synchrotron and X-ray free-electron lasers (XFELs) light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes. Regardless of the application, the basic concept is the same: data collected in early stages of an experiment, data from past similar experiments, and/or data simulated for the upcoming experiment are used to train machine learning models that, in effect, learn specific characteristics of those data; these models are then used to process subsequent data more efficiently than would general-purpose models that lack knowledge of the specific dataset or data class. Thus, a key challenge is to be able to train models with sufficient rapidity that they can be deployed and used within useful timescales. We describe here how specialized data center AI systems can be used for this purpose.


Characterizing machine learning process: A maturity framework

Academic literature on machine learning modeling fails to address how to...

A general-purpose AI assistant embedded in an open-source radiology information system

Radiology AI models have made significant progress in near-human perform...

Electrical Load Forecasting Using Edge Computing and Federated Learning

In the smart grid, huge amounts of consumption data are used to train de...

A Data Quality-Driven View of MLOps

Developing machine learning models can be seen as a process similar to t...

Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models

Most machine learning-based regressors extract information from data col...

Please sign up or login with your details

Forgot password? Click here to reset