In my spare time, I got involved in the MLOps community slack, which is "the" gathering place for anyone interested in the engineering part of productionised machine learning.
As a testimony to the field's emergence (and the free time on everyone's hands in the various lockdowns) in a short couple of months, it gathered more than 3000 members. Among them are enthusiasts of all levels and backgrounds, including students, MLEs, senior product owners of major SV companies and C-levels of VC funded vendors. I highly recommend joining them at https://go.mlops.community/slack.
Demetrios, the community organiser, runs a weekly meetup with various contributors from the MLOps community. In one of our conversations, we recognised the perceived lack of content leadership about the subject of Product Management in Machine Learning. He kindly invited me to the Meetup, which gave me an excellent chance to advocate some of the ideas that this blog and Hypergolic is about:
How did quant finance influence how we ran ML at a London Fintech Startup?
How to solve business problems with incremental ML?
What's the difference between academic and industrial ML?
The Meetup was well received with numerous questions and continued private conversations. If you are a regular reader of my blog, I recommend watching it:
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