SAN FRANCISCO--(BUSINESS WIRE)--Today MLCommons®, an open engineering consortium, released new results from MLPerf™ Training v2.0, which measures the performance of training machine learning models.

Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

MLCommons, a group that develops benchmarks for AI technology training algorithms, revealed the results for a new test that determines system speeds for training algorithms specifically used for the ...

SAN FRANCISCO--(BUSINESS WIRE)--Today, MLCommons ® announced results for its industry-standard MLPerf ® Storage v1.0 benchmark suite, which is designed to measure the performance of storage systems ...

Anyscale today came one step closer to fulfilling its goal of enabling any Python application to scale to an arbitrarily large degree with the launch of Ray 2.0 and the Ray AI Runtime (Ray AIR). The ...

AI/ML can be thought about in two distinct and essential functions: training and inference. Both are vulnerable to different types of security attacks and this blog will look at some of the ways in ...

Training neural networks takes a lot of time, even with the fastest and costliest accelerators on the market. It’s maybe no surprise then that a number of startups are looking at how to speed up the ...

Machine learning (ML) is powering breakthroughs that impact every aspect of our lives: cars that drive themselves, voice assistants that hold conversations, and even software that writes messages and ...