Building Flask APIs for data scientists

Episode #226, published Fri, Aug 23, 2019, recorded Mon, Aug 5, 2019

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.
Links from the show

AJ on Twitter: @pryor_aj
AJ's blog: alanpryorjr.com
AJ's direct email: apryor6@gmail.com
AJ on LinkedIn: linkedin.com
American Tire Distributors blog: medium.com
Job at ATD: Submit your resume to: CoEHiring@ATD-US.com
Flaskerize CLI: github.com/apryor6/flaskerize
Flask_accepts: github.com/apryor6/flask_accepts
Example project using the API structure: github.com/apryor6/flask_api_example
See AJ speak @ Data Science North Carolina 2019, 40% off with code AJP40: dsncconf.com
Presentation on advanced Flask: speakerdeck.com
Original artcile regarding Flask structure: alanpryorjr.com
Episode transcripts: talkpython.fm

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AJ Pryor

AJ is a data scientist, full stack developer, and Ph.D. physicist with expertise in high-performance computing and software engineering who focuses on driving business value through conception and strategic deployment of data-driven ...

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