#226: Building Flask APIs for data scientists

Talk Python To Me

Michael Kennedy

Technology

4.8 • 635 Ratings

🗓️ 23 August 2019

⏱️ 69 minutes

🔗️ Recording | iTunes | RSS

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Summary

If you're a data scientist, how do you deliver your analysis and your models to the people who need them? A really good option is to serve them over Flask as an API. But there are some special considerations you might keep in mind. How should you structure this API? What type of project structures work best for data science and Flask web apps? That and much more on this episode of Talk Python To Me with guest AJ Pryor.

Transcript

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0:00.0	If you're a data scientist, how do you deliver your analysis and your models to the people who need them?
0:04.6	A really good option is to serve them over Flask as an API.
0:08.4	But there are some special considerations you might want to keep in mind.
0:12.2	How should you structure this API?
0:13.9	What type of project structures work best for data science and web apps together?
0:18.4	That and much more on this episode of Talk Python to Me with guest
0:21.5	AJ Pryor. It's episode 226 recorded August 5th, 2019. Welcome to Talk Python to me, a weekly podcast on Python, the language, the libraries, the ecosystem, and the personalities.
0:46.4	This is your host, Michael Kennedy. Follow me on Twitter where I'm at M. Kennedy.
0:50.4	Keep up with a show and listen to past episodes at TalkPython.fm and follow the show on Twitter via At TalkPython.
0:56.7	This episode is brought to you by Linode and Rollbar.
0:59.4	Please check out what they're offering during their segments.
1:01.3	It really helps support the show.
1:03.3	Hey, Jay, welcome to Talk Python to me.
1:04.7	Hi, Michael.
1:05.2	How are you doing?
1:05.7	I'm doing super well.
1:06.7	Thanks for being on the show.
1:07.7	Yeah, absolutely.
1:08.5	I'm super excited. Yeah, it's going to be a lot of fun.
1:18.1	We get to talk about some things that are really popular in Python, web development, and data science. And then we're going to, like, intersect them together, which I don't know is going to make them, like, mega popular.
1:22.4	Because if you look at the Python space, all the surveys and sort of where people are working, it seems like
1:28.7	it's mostly web development or data science and then just like a whole bunch of others, right?
	...

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