Data Science powered APIs with Jupyter

Last year, in august I had the pleasure and the honor to present at the first Jupyter conference in New York, JupyterCon. First of all let me tell you that Jupyter is turning into a great ecosystem of tools aiming for higher productivity in data science, and the community of developers, and users is really amazing, vibrant and very welcoming.

My contribution for this conference was about exposing HTTP Web APIs straight from Jupyter Notebooks. In that occasion I have presented a number of demo's about this can be done using the Jupyter Kernel Gateway.

Jupyter notebooks are transforming the way we look at computing, coding, and science. But is this the only "data scientist experience" that this technology can provide? Definitely not. During my presentation I have used Jupyter Notebooks and the Jupyter Kerne to create interactive web applications for data exploration and machine learning. In the background this code is still powered by the well-understood and well-documented Jupyter Notebooks.?

If you wish you test and experiment yourself, I have shared the code on github: https://github.com/natbusa/kernelgateway_demos

Here below a screen shot I have taken on a classifier app for the bespoken iris dataset.

I would like to thank the O'Reilly for supporting Jupyter and the amazing Jupyter community and knowledgable speakers, with a special thanks to Peter Parente (Jupyter), Min RK (Jupyter Hub), Fernardo Perez (Jupyter / IPython),?Rachel Thomas (fast.ai), Peter Wang (Anaconda), Wes McKinney (Pandas and Apache Arrows), Paco Nathan (O'Reilly Learning Platform) with their outstanding contributions.