How I Became a Machine Learning Practitioner • Hacker News Board

Re: How I Became a Machine Learning Practitioner

#11

Post by dhairya » Tue, Jul 30, 2019, 8:18 PM UTC

streetcat1 wrote: post #9

dhairya wrote: post #7

Earlier quoted context omitted.

Yes but the transition definitely doable and his advice is great. The key part of his advice is spending time experimenting, rapidly failing, and continuing to work on it with real world use case. Often the challenge is making the jump from the simple toy examples used in educational materials to the messiness of real-world data. I'm a senior data scientist at vc-back startup. I'm in a hybrid data scientist/ machine…

Thanks for the info. I have a serious question (not for bashing) Can you please describe what part of your job CANNOT be automated?

No worries, fair question. It worth noting that my job is not data analysis. So I do use data analysis to evaluate our metrics and model performance.

Really none of it is really automatable. I'm working developing NLP features for our product (question answering, search, neural machine translation, dialog, etc). Our customer data is diverse, in different formats, and thier use cases are all distinct. So most of my work is novel applied research and development.

Re: How I Became a Machine Learning Practitioner

#12

Post by streetcat1 » Tue, Jul 30, 2019, 8:29 PM UTC

dhairya wrote: post #11

streetcat1 wrote: post #9

Earlier quoted context omitted.

Thanks for the info. I have a serious question (not for bashing) Can you please describe what part of your job CANNOT be automated?

No worries, fair question. It worth noting that my job is not data analysis. So I do use data analysis to evaluate our metrics and model performance. Really none of it is really automatable. I'm working developing NLP features for our product (question answering, search, neural machine translation, dialog, etc). Our customer data is diverse, in different formats, and thier use cases are all distinct. So most of my wo…

Thanks.

I do assume that the data format is different (alas I also assume that they are all some sort of a text file with known fields and types).

But after you setup the dataset definition and defined the schema, the rest can be based on neural search?

Moreover, isn't there a state of the art architecture for each of the task. E.g. Seq2Seq for machine translation. Can you just use that as a base line, and let the NAS engine search hyper param, etc?

Re: How I Became a Machine Learning Practitioner

#13

Post by jorblumesea » Tue, Jul 30, 2019, 8:44 PM UTC

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

Re: How I Became a Machine Learning Practitioner

#14

Post by jph00 » Tue, Jul 30, 2019, 8:46 PM UTC

dhairya wrote: post #10

copperx wrote: post #3

Earlier quoted context omitted.

Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.

So there is a difference between ML research and application. Being a practitioner doesn't require deep math knowledge that perhaps research would. Jeremy Howard's fastai course is a great example of how someone with a solid programming background can effectively transition into being a deep learning practitioner. Given that production ml and deep learning is still the wild west, as a practioner, you can contribute a…

Also, note that Greg's goal was to contribute to OpenAI's flagship project. That's a rather ambitious goal!

Also, most folks I know that are making practical deep learning contributions are doing so by combining their pre-existing domain expertise with their new deep learning skills. E.g. a journalist analyzing a large corpus of text for a story, or an oil&gas analyst building models from well plots, etc.

Re: How I Became a Machine Learning Practitioner

#15

Post by frenchman99 » Tue, Jul 30, 2019, 8:48 PM UTC

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

Well, you could save money and use it to feed yourself while learn new skills when you're out of a job. That works too.

Re: How I Became a Machine Learning Practitioner

#16

Post by eanzenberg » Tue, Jul 30, 2019, 8:51 PM UTC

streetcat1 wrote: post #6

copperx wrote: post #3

Earlier quoted context omitted.

Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.

Maybe if you want to write tensorflow, but not if you want to use it.

If you want to write TF, then you are a ml engineer. If you want to use TF, then you are a scientist (data, research, whatever)

Re: How I Became a Machine Learning Practitioner

#17

Post by mastratton3 » Tue, Jul 30, 2019, 8:55 PM UTC

dhairya wrote: post #7

minimaxir wrote: post #2

Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.

Yes but the transition definitely doable and his advice is great. The key part of his advice is spending time experimenting, rapidly failing, and continuing to work on it with real world use case. Often the challenge is making the jump from the simple toy examples used in educational materials to the messiness of real-world data. I'm a senior data scientist at vc-back startup. I'm in a hybrid data scientist/ machine…

This is really good advice. I'd say it generalizes to learning most engineering challenges: Pick a problem you're interested in and solve it from top to bottom, tweaking all components as you move forward.

Re: How I Became a Machine Learning Practitioner

#18

Post by klipt » Tue, Jul 30, 2019, 8:55 PM UTC

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

There's plenty of non ML software engineering to be done. Anecdotally a large proportion of interns want an "ML project", but only a small percentage of teams looking for interns are offering one.

Too many people going into ML could skew the supply/demand into making it a worse job option (more work, less pay), like game programming or academia.

Re: How I Became a Machine Learning Practitioner

#19

Post by sgt101 » Tue, Jul 30, 2019, 8:58 PM UTC

minimaxir wrote: post #2

Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.

We know that someone with a good CS degree can do this because.. Ph.D students....

Re: How I Became a Machine Learning Practitioner

#20

Post by seanmcdirmid » Tue, Jul 30, 2019, 9:23 PM UTC

klipt wrote: post #18

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

There's plenty of non ML software engineering to be done. Anecdotally a large proportion of interns want an "ML project", but only a small percentage of teams looking for interns are offering one. Too many people going into ML could skew the supply/demand into making it a worse job option (more work, less pay), like game programming or academia.

There aren’t that many engineers with 10+ years of ML experience ATM, but there will be tons in 5 or so years. Chasing tomorrow’s trend is always more productive than chasing today’s, but of course the former requires predicting the future.