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Originally published in Chip Huyen Blog, June 22, 2020
To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:
After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that aren’t being actively developed, and tools that nobody uses, I got 202 tools. See the full list. Please let me know if there are tools you think I should include but aren’t on the list yet!
This list was made in November 2019, and the market must have changed in the last 6 months.
Some tech companies just have a set of tools so large that I can’t enumerate them all. For example, Amazon Web Services offer over 165 fully featured services.
There are many stealth startups that I’m not aware of, and many that died before I heard of them.
This post consists of 6 parts:
I. OverviewII. The landscape over timeIII. The landscape is under-developedIV. Problems facing MLOpsV. Open source and open-coreVI. Conclusion
In one way to generalize the ML production flow that I agreed with, it consists of 4 steps:
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