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Amazon today debuted AWS Trainium, a chip custom-designed to deliver what the company describes as cost-effective machine learning model training in the cloud. It comes ahead of the availability of ...

How much time is your machine learning team spending on labeling data — and how much of that data is actually improving model performance? Creating effective training data is a challenge that many ML ...

Facebook has to keep digging into ever-lower levels of its architecture to make efficient use of endlessly growing training data. This has meant rethinking how ML training pipelines operate from ...

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 ...

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 ...

Former Intel Corp. executives Naveen Rao and Hanlin Tang have gotten back into the startup game, announcing the launch of a new company called MosaicML today that promises to optimize machine learning ...

Open engineering consortium MLCommons has released new results from MLPerf Training v2.0, which measures how fast various platforms train machine learning models. The organizations said the latest ...

In my April SemiEngineering Low Power-High Performance blog, I wrote: “Today, AI/ML neural network training models can exceed 10 billion parameters, soon it will be over 100 billion.” “Soon” didn’t ...

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 ...