After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, I noticed two groups of companies. One group has made significant investments (hundreds of millions of dollars) into infrastructure to allow real-time machine learning and has already seen returns on their investments. Another group still wonders if there’s value in real-time ML.
There seems to be little consensus on what real-time ML means, and there hasn’t been a lot of in-depth discussion on how it’s done in the industry. In this post, I want to share what I’ve learned after talking to about a dozen companies that are doing it.
There are two levels of real-time machine learning that I’ll go over in this post.
I use “model” to refer to the machine learning model and “system” to refer to the infrastructure around it, including data pipeline and monitoring systems.
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