One of the challenges I’ve encountered when coaching data science teams in smaller organisations is the difficulty of publishing proof-of-concept data science products via web calls, when the team doesn’t know anything about web programming. My preference is to use Flask (and flask-restful and maybe Swagger docs) but that’s an awful lot of learning to put onto a non-engineering researcher to help them publish code that another team can consume.
I’ve prototyped “featherweight” as a very simple solution to this problem. Behind the scenes Flask is used to publish your function(s) on a local server. You can then call the function with standard GET requests and key/value arguments (e.g. via cURL or a web browser or the requests module) and get a block of JSON that wraps whatever results your function returned.
The goal is to make it super-easy for a non-engineering researcher to take their Python function or method and to publish it on a web API, without knowing anything about web programming. Examples on github include publishing a simple math function and publishing scikit-learn’s Iris classifier.
Whilst this API won’t solve production use-cases (it is single-threaded, it doesn’t do any clever logging, there’s no additional security) it will solve proof-of-concept and dev-level usage. It also opens the door to moving from Featherweight to a custom Flask interface. Feedback happily received!
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.
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