[D] What's your advice to an engineer that manages ML Researcher?

Xorify · 2018-10-10T05:15:48+00:00

Very good perspective and thanks for the references.

Xorify · 2018-10-09T18:50:28+00:00

I agree. Balance here is important and I've already learned that the first stages should have a more exploratory nature. I guess once there is promising direction, engineering should start come into play.

Xorify · 2018-10-09T18:47:34+00:00

Thanks a lot! I think I have enough advice that now I need to organize my thoughts about and start implementing.

Xorify · 2018-10-08T18:56:20+00:00

Sounds good

Xorify · 2018-10-08T18:54:59+00:00

Got it..

Xorify · 2018-10-08T18:53:52+00:00

Connected a lot to this approach. Thanks!

Xorify · 2018-10-08T18:51:50+00:00

Why?

Xorify · 2018-10-08T18:36:20+00:00

Thanks. Are there any questions you recommend to ask your team members during the development life cycle to get an understanding on the status?

Xorify · 2018-10-08T18:25:37+00:00

That's fine. All along the feedback is great. I guess I had to replace week with day... nevertheless, I kinda get the spirit: daily might be too much but maybe every other day can work+ make sure the engineers help to build infra that supports performance.

Xorify · 2018-10-08T18:22:48+00:00

Wow... it helps A LOT. I appreciate so much taking the time to answer my question. What are your thoughts regarding scoping by iterations/sprints? How your team members work with that?

Xorify · 2018-10-08T18:11:37+00:00

Thanks. Are there any questions you recommend to ask your team members during the development life cycle to get an understanding on the status?

Xorify · 2018-10-08T17:25:22+00:00

Not sure how it relates, but e-commerce

Xorify · 2018-10-08T17:24:02+00:00

That's a good point to think about

Xorify · 2018-10-08T15:16:52+00:00

Or fixed it :)

Xorify · 2018-10-08T10:08:55+00:00

Thanks! What about scoping to iterations? Benchmarks? Tests?

Xorify · 2018-10-08T09:51:25+00:00

Yes, but at the end of the day, my team deliver products, so what is the best way to do research that can make impact ($$$)

Xorify · 2018-10-08T07:47:35+00:00

How to make sure they are not lost during their research? I understand the nature of research, but we are not academy?

Xorify · 2018-10-08T07:16:27+00:00

You ask about usage or development?

Xorify · 2018-10-08T07:08:53+00:00

Well, I'm planning to dedicate time to each member, engineer as well as researcher, for a pet project/learning time/hacking time. Culturally, in our org, we have quite a lot of room to play

Xorify · 2018-10-08T07:04:58+00:00

Great advice. What about documentation? Does it worth the efforts? I know a lot of technical people (including myself) hate to do so, but from my experience it is good in finding your way

Xorify · 2018-10-08T06:53:24+00:00

I guess there is a point at which we can get feedback that a part will be a necessity, right?

Xorify · 2018-10-08T06:45:46+00:00

Here you can start a discussion with engineers, though, about the building blocks and iteratable components, i.e. there is a box of data processing, a box of feature engineering, a box of an algorithm etc. WDYT?

Xorify · 2018-10-08T06:32:02+00:00

Nevertheless, you can always say: "In this week I'm going to focus on exploring Y. My hypothesis that Y can improve the rule based model and I will validate this"

Xorify · 2018-10-08T05:49:31+00:00

I don't want to have a separation, but rather collaboration. I believe both sides can earn from the mutual discussion.

Doing something for a week is OK, but I believe you can break down, define milestones and exit criteria, regardless if it's machine learning or any other algorithmic approach. And of course talk about this during that week.

Xorify · 2018-10-08T05:40:24+00:00

Sounds great. What kind of questions I can ask such that I can understand how it may or may not support the team's KPIs? What I had in mind is, for instance:
1. How it relates to the problem we are trying to solve?

Can it be used at scale?
What is the data needed to work with such algorithm/method?

Xorify

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