Organizations are turning to machine learning because of the return on investment. The ones doing it in real-time are topping the charts.

After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, two groups of companies emerged. One group has invested hundreds of millions of dollars into infrastructure to allow real-time machine learning and has already seen returns on their investments. The other group still wonders if there’s value in real-time machine learning.

The fact that reporting on return on investment is a good way to get attention does not seem to be lost on Chip Huyen. Huyen is a writer and computer scientist who works on infrastructure for real-time machine learning. She is the one who wrote the above introduction to her findings on real-time machine learning in order to crystallize the growing experience she and her colleagues are accumulating.

Huyen has worked with the likes of Netflix, Nvidia, Primer and Snorkel AI before founding her own (stealth) startup. She is a Stanford graduate, where she also teaches Machine Learning Systems Design and was a LinkedIn Top Voice in 2019 and 2020.

In other words, Huyen is very well-positioned to report on what fellow ZDNet contributor Tony Baer described as “a long-elusive goal for operational systems and analytics” in his data 2022 outlook: unifying data in motion (streaming) with data at rest (data sitting in a database or data lake). The ultimate goal in doing that would be to achieve the kind of ROI Huyen reports on.

Read the full article on ZDNet