5 ms·

Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!

minimaxir 8y ago
There's been data science/machine learning bootcamps around for awhile (Galvanize/Metis being common examples in San Francisco), but apparently job placement is not in a good place (as with normal bootcamps). Indeed Machine Learning/Deep Learning has become much more accessible thanks to the number of free guides such as this. But that means data science job placement will become more difficult as competition increases, with more gatekeeping/requirements (e.g. Masters/Ph.Ds)
- alexbeloi 8y ago
  The issues I've heard from a few people in hiring is that there is a surplus of junior data scientists from these camps and a shortage of senior data scientists to manage them. Problems not dissimilar to tech hiring in general, but companies need a lot more SWEs than data scientists.
- plaguuuuuu 8y ago
  Depends what the company is doing. Most companies are going to utilise ML to some extent. Once technology and tooling improves they'll need boots on the ground engineers and not labs with R&D teams
  - minimaxir 8y ago
    Masters/PhDs can be boots-on-the-ground engineers too.
amelius 8y ago
Yes, deep learning is what web-development was twenty years ago (and now everybody and their mother can build a website).
- jondwillis 8y ago
  Too bad ML as a service is already largely cornered by $FANG
  - gaius 8y ago
    Too bad ML as a service is already largely cornered by $FANG Wat? Neither Facebook nor Netflix offer outsiders access to their ML platform, and you completely forgot Azure, which IMHO has the most mature offering of the big 3 in this space.
Puer 8y ago
Honestly, unless these ML bootcamps are extensive courses on calculus, linear algebra, and statistics and not just "Here's k-means. Memorize it" I doubt they'll harm the market for grad school educated data scientists.
- rs86 8y ago
  Agreed
- tyzz 8y ago
  I'd like to offer a counterpoint. I attended one of the machine learning bootcamp mentioned above, and it was transformative for me. I got hired within a month, doubled my salary to over 100k, and landed a job that I enjoy and find intellectually stimulating. All this while having little to no technical experience (only math I took in college was intro to stats, and my pre-bootcamp career was in a non-technical capacity). I completely understand why there is such a stigma around bootcamps. Nobody can deny that they don't afford the same depth that you'd get at a "real" program. But they can be amazing for career switchers like me, who had no real direction in college. Don't look down your nose at them.
closeparen 8y ago
Have boot camps noticeably suppressed wages for software engineering in general?
jorgemf 8y ago
You cannot learn machine learning or deep learning in a few months. You can learn to copy what these guides do, but if you want to do something slightly different you will feel you know nothing (because you actually probably don't know anything about the maths behind why the things works, so when you want to change them you don't know how)
- mindcrime 8y ago
  I don't deny that knowing the math / theory is useful, but wonder if we sometimes overestimate the degree to which it is essential. For example, backprop with SGD is a good foundation for many, many, many applications of NN's, and pre-built implementations exist that let you use the technique without understanding the details of the math. And with those tools, you can experiment with many different combinations of features, different architectures, etc. Of course understanding the theory will be helpful in knowing which architectures are most likely to be productive and what-not, but this whole field is very empirical anyway. So if your experimenting is a little less guided my intuition rooted in theory, that's not exactly the end of the world.
  - kk58 8y ago
    Reason you need an education in this theory is twofold. How to fix something that is broken in limited time? How to assure this model is reliable? To confidently answer this from a place of reason derived from theory is going to be the real value.
    - mindcrime 8y ago
      Sure, but it's a continuum, not a binary dichotomy. Just like you can do more with your car if you have degrees in mechanical engineering and fluid dynamics, but a person with nothing but a high-school diploma can upgrade a camshaft. The point is, you can do a lot of very useful things with ML, without needing the entirety of the theoretical underpinnings. Of course you can't do everything but not everybody needs to be able to do everything.

minimaxir 8y ago

There's been data science/machine learning bootcamps around for awhile (Galvanize/Metis being common examples in San Francisco), but apparently job placement is not in a good place (as with normal bootcamps). Indeed Machine Learning/Deep Learning has become much more accessible thanks to the number of free guides such as this. But that means data science job placement will become more difficult as competition increases, with more gatekeeping/requirements (e.g. Masters/Ph.Ds)

alexbeloi 8y ago
The issues I've heard from a few people in hiring is that there is a surplus of junior data scientists from these camps and a shortage of senior data scientists to manage them. Problems not dissimilar to tech hiring in general, but companies need a lot more SWEs than data scientists.
plaguuuuuu 8y ago
Depends what the company is doing. Most companies are going to utilise ML to some extent. Once technology and tooling improves they'll need boots on the ground engineers and not labs with R&D teams
- minimaxir 8y ago
  Masters/PhDs can be boots-on-the-ground engineers too.

amelius 8y ago

Yes, deep learning is what web-development was twenty years ago (and now everybody and their mother can build a website).

jondwillis 8y ago
Too bad ML as a service is already largely cornered by $FANG
- gaius 8y ago
  Too bad ML as a service is already largely cornered by $FANG Wat? Neither Facebook nor Netflix offer outsiders access to their ML platform, and you completely forgot Azure, which IMHO has the most mature offering of the big 3 in this space.

Puer 8y ago

Honestly, unless these ML bootcamps are extensive courses on calculus, linear algebra, and statistics and not just "Here's k-means. Memorize it" I doubt they'll harm the market for grad school educated data scientists.

rs86 8y ago
Agreed
tyzz 8y ago
I'd like to offer a counterpoint. I attended one of the machine learning bootcamp mentioned above, and it was transformative for me. I got hired within a month, doubled my salary to over 100k, and landed a job that I enjoy and find intellectually stimulating. All this while having little to no technical experience (only math I took in college was intro to stats, and my pre-bootcamp career was in a non-technical capacity). I completely understand why there is such a stigma around bootcamps. Nobody can deny that they don't afford the same depth that you'd get at a "real" program. But they can be amazing for career switchers like me, who had no real direction in college. Don't look down your nose at them.

closeparen 8y ago

Have boot camps noticeably suppressed wages for software engineering in general?

jorgemf 8y ago

You cannot learn machine learning or deep learning in a few months. You can learn to copy what these guides do, but if you want to do something slightly different you will feel you know nothing (because you actually probably don't know anything about the maths behind why the things works, so when you want to change them you don't know how)

mindcrime 8y ago
I don't deny that knowing the math / theory is useful, but wonder if we sometimes overestimate the degree to which it is essential. For example, backprop with SGD is a good foundation for many, many, many applications of NN's, and pre-built implementations exist that let you use the technique without understanding the details of the math. And with those tools, you can experiment with many different combinations of features, different architectures, etc. Of course understanding the theory will be helpful in knowing which architectures are most likely to be productive and what-not, but this whole field is very empirical anyway. So if your experimenting is a little less guided my intuition rooted in theory, that's not exactly the end of the world.
- kk58 8y ago
  Reason you need an education in this theory is twofold. How to fix something that is broken in limited time? How to assure this model is reliable? To confidently answer this from a place of reason derived from theory is going to be the real value.
  - mindcrime 8y ago
    Sure, but it's a continuum, not a binary dichotomy. Just like you can do more with your car if you have degrees in mechanical engineering and fluid dynamics, but a person with nothing but a high-school diploma can upgrade a camshaft. The point is, you can do a lot of very useful things with ML, without needing the entirety of the theoretical underpinnings. Of course you can't do everything but not everybody needs to be able to do everything.