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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
What is a data lake in basic terms?
Got messaged by a C3 . ai recruiter. Read that wlb is bad and that the interview process is absurdly long, but the Glassdoor reviews are 4.2 and can't find actual hours worked posted by anyone. How's the culture really? I'd be aiming for DS consulting, something more functional but with DS/ML concepts as my differentiator.
C3.ai, Inc.
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Hey! Not sure what level of pipeline your asking about (specifically in terms of data size and currency), but, since you’re asking here, you might mean reporting oriented data pipelines, Guy In A Cube on YouTube is a great resource for all things Power BI related and they have a pretty good collection of videos on ETL topics involving configuring presentation data layers. Even if you’re not using Power BI you might find some of their content helpful.
Thank you. I actually meant designing data/etl pipelines in the cloud such as aws/gcp using devops.
Gotcha, can’t say I have recommendation here, but I’m interested too!