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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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All the time. The infrastructure is often already ready for me on larger projects. Occasionally, I'll have to set it all up myself, but those are more just prototypes/MVPs.
I worked for a mid size company with the only DS in the company. I built DS product and had to deploy them as well
Yeah I did in consulting. Deployed both managed services and on-prem stuff for clients. I liked the managed service route where we control everything. Much easier when you control everything.
Nah. I just don't like having to plan for getting through opaque client acceptance reviews. You can get in turf wars with some of that stuff.
Yes.. and i didnt have the enough knowledge to deploy it to GCP.. had to do the cert
Most data science teams doing real work have to deploy into production. It does require properly staffing and building stronger IT relationships than simply getting data and environment set up.
What do you all use for model deployment?
Absolutely, all the time. End to end across our team.