[elliott34]

I think the best part of this article is the approach to learning new things he presents--going beneath the surface, learning the fundamentals below the “API” layer (abstractly speaking). Really great explanation of that insight!

The part I am having trouble with, with ~5 years of real world data science experience in industry, is the implicit assumption that we all need or want to become good at deep learning.

In my experience, most businesses, at best, are still struggling with overfitting logistic regression in Excel let alone implementing/integrating it with a production code base. And we all know that toy ML models that sit on laptops create ZERO value beyond fodder for Board presentations or moving the CMO's agenda forward.

The fact of the matter is that the vast majority of businesses, with respect to statistics/ML, aren’t doing super duper basic shit (like a random forest microservice that scores some sort of transaction) that might increase some metric 10%. This is due to lack of sophisticated analytics infrastructure/bureaucracy/ lack of talent/ being too scared of statistics. Ultimately when you’re rolling out a machine learning product internally (I’m not talking about Azure/ other aws-model-training-as-a-service type things), the hardest part isn’t: “We need to increase our accuracy by 2% by using Restricted Convolutionallly Recurrent Bayesian Machines!” The hardest part is convincing people you need to integrate a new process into a “production” workflow, and then maintaining that process.

[rm999]

>the hardest part isn’t: “We need to increase our accuracy by 2% by using Restricted Convolutionallly Recurrent Bayesian Machines!” The hardest part is convincing people you need to integrate a new process into a “production” workflow, and then maintaining that process.

Completely agreed. My timeline for a project usually goes like:

A. 2-4 weeks: deeply understand the problem, talk to stakeholders, gather requirements, plan out the project.

B. 2 weeks: explore the problem and the data. Build and tweak models, build a functional prototype.

C. 8-24 weeks: put the system into production on top of the companies' tech stack, either myself or working closely with engineers.

D. 4-12 weeks: sell the system internally, prove that it's a superior solution, get buy-in that it should replace existing processes.

So yeah, in a typical 6 month project I only spend about 5-10% of my time on actual data and modeling. This % has gone sharply down as my career has progressed.

[kwisatzh]

Totally agree! If I had a dollar bill every time I had a senior exec (VP-level) for large orgs ask me what 'probability' means, I wouldn't need to be a data scientist anymore! The issue is deeper than what you write I think. There is technical debt of course in implementing complex ML/DS pipelines that should be compensated by the increase in lift/revenue. Outside of large companies like Google/FB/Apple etc who have incorporated ML in their products, many outfits that want to use their 'data' to 'address business problems' don't really need sophisticated ML or can justify the technical and human debt. Having worked in the industry as a data scientist, I'm not too hopeful about the prospects of many DS-as-a-service companies, not because there isn't solid technical content to offer, but their clients are routinely idiots.

[woah]

Are you saying everyone is an idiot, including the customers of data science companies, or that people who pay for data science are especially idiotic?