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Beware the data science pin factory: The power of the data science generalist

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Re: Beware the data science pin factory: The power of the data science generalist

#71

Post by dchichkov »

mlthoughts2018 wrote: post #64
dchichkov wrote: post #49

Earlier quoted context omitted.

My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent". By the way, I don't see how anyone could have more than a decade specifically in deep learning, considering that the field had started at around that time. On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon. Who cares, if a…

> “My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent".” Yes, same for me. This builds in nearly a decade of preparatory work into the timeline... so it seems we agree. > “On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon.” I work professionally in deep learning for image pro…

Have you used AutoML in practice for DNN architecture search?

Re: Beware the data science pin factory: The power of the data science generalist

#72

Post by gk1 »

thekhatribharat wrote: post #37
gk1 wrote: post #33

Earlier quoted context omitted.

You'll have to do better than an ad hominem + "the opposite is true." Author is Chief Algorithms Officer at Stitchfix and former VP Data Science & Engineering at Netflix.

You'll have to do better than an argumentum ab auctoritate (aka argument from authority ) PS: Thanks for pointing out ad hominem :)

I like Ray Dalio's principle of "believability."[1] All else being equal, it's reasonable to weight an experienced person's input more than that of a less experienced person.[2]

[1]https://medium.com/@pblanken/considering-believability-cf604...

[2] I don't know the experience of the person I was responding to, so I'm making an assumption.

Re: Beware the data science pin factory: The power of the data science generalist

#73

Post by petty_griper »

PointersAreFun wrote: post #69
School-Cotton wrote: post #67

Earlier quoted context omitted.

This would be unnecessarily harsh even if it were true. It’s not, though. I know a data scientist who studied Arabic language. And software engineers who studied music.

I feel bad for whoever employed them. People manage to get into jobs they are unqualified for all the time. Music and Computer Science/Mathematics are two very different qualifications. Why would someone who spent their time in college partying, because that’s what Art/Music majors do, and avoiding STEM like the plague be employed as a Data Scientist or any scientist position? They don’t have the background or dilige…

There are plenty of studious music majors, plenty of liberal arts majors capable of catching up with their C.S. major peers with a year or so of dedicated study, and plenty of people who party hard and are still qualified and capable to do the work that you do.

Your comment reads more like sour grapes about your own college experience than anything else ;)

Re: Beware the data science pin factory: The power of the data science generalist

#74

Post by dang »

PointersAreFun wrote: post #65
reilly3000 wrote: post #7

I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch…

Why would anyone hire you? You want a scientist role without the qualifications. Go get a degree in Computer Science or Mathematics, then try again. It is your fault for majoring in music. Though you might find a Data Musician role, I don’t know?

Personal attacks are not ok here, and we've banned this account.

If you don't want to be banned, you're welcome to email hn@ycombinator.com and give us reason to believe that you'll follow the rules in the future.

https://news.ycombinator.com/newsguidelines.html

Re: Beware the data science pin factory: The power of the data science generalist

#75

Post by mlthoughts2018 »

dchichkov wrote: post #71
mlthoughts2018 wrote: post #64

Earlier quoted context omitted.

> “My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent".” Yes, same for me. This builds in nearly a decade of preparatory work into the timeline... so it seems we agree. > “On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon.” I work professionally in deep learning for image pro…

Have you used AutoML in practice for DNN architecture search?

Yes, I have used AutoKeras in practice, with mixed results. I have also written in-house hyperparameter search tooling to spread parametric architecture search in a distributed training environment with about the same mixed success. I have done this for both large-scale image processing networks and natural language processing networks.

Using AutoML in practice is beyond foolish, given the pricing, except for a really small minority of customers. Let alone that neural architecture search is not a silver bullet and frequently is totally not helpful for model selection (for example, say your trade-off space involves severe penalty on runtime and you have a constraint that your deployed runtime system must be CPU-only.. you may trade performance for the sake of reducing convolutional layers, in a super ad hoc business-driven way that does not translate to any type of objective function for NAS libraries to optimize... one of the most important production systems I currently work on has exactly this type of constraint).

Re: Beware the data science pin factory: The power of the data science generalist

#76

Post by dchichkov »

mlthoughts2018 wrote: post #75
dchichkov wrote: post #71

Earlier quoted context omitted.

Have you used AutoML in practice for DNN architecture search?

Yes, I have used AutoKeras in practice, with mixed results. I have also written in-house hyperparameter search tooling to spread parametric architecture search in a distributed training environment with about the same mixed success. I have done this for both large-scale image processing networks and natural language processing networks. Using AutoML in practice is beyond foolish, given the pricing, except for a reall…

Interesting. I agree, it is not trivial to estimate the runtime of the model on a target device. I wonder how Google does it. They've been boasting about precisely this ability - to optimize for architecture under constraints of precision AND runtime for a target device. And then, claiming that they've been able to get an architecture better than one optimized by a team of engineers over a few years.

Re: Beware the data science pin factory: The power of the data science generalist

#77

Post by mlthoughts2018 »

dchichkov wrote: post #76
mlthoughts2018 wrote: post #75

Earlier quoted context omitted.

Yes, I have used AutoKeras in practice, with mixed results. I have also written in-house hyperparameter search tooling to spread parametric architecture search in a distributed training environment with about the same mixed success. I have done this for both large-scale image processing networks and natural language processing networks. Using AutoML in practice is beyond foolish, given the pricing, except for a reall…

Interesting. I agree, it is not trivial to estimate the runtime of the model on a target device. I wonder how Google does it. They've been boasting about precisely this ability - to optimize for architecture under constraints of precision AND runtime for a target device. And then, claiming that they've been able to get an architecture better than one optimized by a team of engineers over a few years.

It’s all hype coming out of Google. Most of this stuff is meant for foisting overpriced solutions onto unwitting GCP customers who get burnt by vendor lock-in and don’t have enough in-house expertise to vet claims about e.g. overpriced TPUs or overpriced AutoML.

Re: Beware the data science pin factory: The power of the data science generalist

#78

Post by jonathankoren »

mlthoughts2018 wrote: post #63
jonathankoren wrote: post #57

Earlier quoted context omitted.

>You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems. No. Even these people haven’t been doing it for a decade.

Yes. They were doing 4 years of math intensive undergrad and 5+ years of math intensive post-grad work before there even was an advent of deep learning in 2008-2012.

So they were doing deep learning as an undergrad, huh? Studying tensors for 10 years? Next you’ll be telling me they have 20 years experience coding for CUDA.

No. None of these people have the math background you think, nor do you need it.

Re: Beware the data science pin factory: The power of the data science generalist

#79

Post by jonathankoren »

jonathankoren wrote: post #78
mlthoughts2018 wrote: post #63

Earlier quoted context omitted.

Yes. They were doing 4 years of math intensive undergrad and 5+ years of math intensive post-grad work before there even was an advent of deep learning in 2008-2012.

So they were doing deep learning as an undergrad, huh? Studying tensors for 10 years? Next you’ll be telling me they have 20 years experience coding for CUDA. No. None of these people have the math background you think, nor do you need it.

I don’t want to belabor this point (but apparently I do, since I’m replying to my own post 2 hours later), but your idea of going back and counting undergraduate work — when it isn’t even related — is simply padding for padding sake. Why stop there? Why not 4 years of high school, too? Aren’t they prerequisites? I mean you can go back all the way to preschool, since counting is a prerequisite to math, and hell, is a whole section of discrete math. But you don’t, because claiming you need 25 years of mathematical training sounds ludicrous.
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