This document discusses Jupyter widgets, which allow building user interfaces entirely in Python within Jupyter notebooks. It notes that developing machine learning software is difficult as it is often stochastic, black box, and prone to overfitting. Traditional software development workflows are inadequate for machine learning. Jupyter widgets aim to address this by enabling data scientists to rapidly create user interfaces for exploring models within their familiar Jupyter environment using Python. Examples of Jupyter widgets libraries are provided.

2. ASI Data Science is a
London-based data science
consultancy

3. About me
— Committer for Jupyter widgets
— Main author of jupyter-gmaps, a library for
visualizing geographical data in Jupyter notebooks
— Author of Scala for data science (Packt Publishing)

4. Jupyter widgets
for human-in-the-loop data
science

6. Developing machine
learning software is hard

7. Developing machine learning software is hard
— almost always stochastic
— often black box

8. Developing machine learning software is hard
— robustness
— overfitting
— overreliance on certain features or groups of features

9. Traditional software
development workflows are
inadequate

10. Human intuition
It should be easy for humans to explore the model.

11. Human intuition
Computers think in terms of bytes and instructions, and
humans think in terms of concepts and images.

12. Human intuition
We need a framework to rapidly create UIs that allow
the human to think at a higher level of abstraction.
The UI should not be a black box.

13. Jupyter widgets

15. Jupyter widgets allow building user interfaces entirely in
Python, directly in Jupyter notebooks.

16. Build user interfaces in
Python
directly in
Jupyter notebooks

17. Examples

18. Jupyter widgets
— Jupyter widgets are written entirely in Python
— They are written in the environment the data
scientist is currently working in
— Widgets have access to the entire state of the
notebook

20. Ecosystem

21. Core: ipywidgets

22. bqplot

23. ipyleaflet and gmaps

24. qgrid

25. ipyvolume

26. Learning about widgets
— mlviz: visualising machine learning algorithms with
Jupyter widgets and bqplot.
— Jupyter widgets tutorial
— Coding a simple widget from scratch: video and code
— Jupyter widgets documentation

27. Libraries used in this talk
— ipywidgets
— bqplot
— gmaps
— lens
— superintendent

28. Use widgets to
reduce friction
at the human computer
interface

29. Acknowledgements
— Jupyter widgets developers: Jason Grout, Sylvain
Corlay, Maarten Breddels, Matt Craig, Vidar Tonaas
Fauske
— ASI Data Science
— Chakri Cherukuri (ChakriCherukuri)
— Victor Zabalza (zblz) and Scott Stevenson (srstevenson)
— Jan Freyberg (janfreyberg)
— SherlockML