[HN Gopher] Machine Learning Cohorts: A Synthesis
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       Machine Learning Cohorts: A Synthesis
        
       Author : polm23
       Score  : 13 points
       Date   : 2021-06-20 13:23 UTC (1 days ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | seasily wrote:
       | This largely misses the mark. Kaggle is a machine learning
       | competition platform for a small set of exceptional machine
       | learning talent, and a lot of students or hangers-on.
       | 
       | "Machine learning projects - if ML is being attempted at all -
       | are in early stages, using traditional methods that are best-
       | suited for high-RAM CPU rather than GPU SKUs (ex: scikit-learn
       | and clustering approaches)."
       | 
       | The idea that the machine learning being done there is in "early
       | stages" is laughable, given the prize pools and sheer
       | competitiveness usually move well past existing SOTA--usually
       | moving forward benchmarks on Google's image classification and
       | labeling (!) benchmarks and other areas where enormous teams
       | can't match the top few.
       | 
       | Part of what you're seeing is the mass of survey participants,
       | who show up to fork notebooks and fake ML skills, are the people
       | who haven't exactly established themselves in the field
       | (https://commons.m.wikimedia.org/wiki/File:Survivorship-
       | bias....), while the top-end is too small a group to fully
       | characterize with low-powered clustering.
       | 
       | Even five years ago, most real-world MLEs knew how to use AWS,
       | were deploying actual machine learning models (granted, more
       | primitive than today's methods), and I'm wouldn't be so glib
       | about calling anything "early stage" even then given the raw
       | business value it provides.
        
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       (page generated 2021-06-21 23:01 UTC)