Human-in-the-loop data science with Jupyter widgets
Jupyter widgets let you create lightweight, interactive graphical interfaces directly in Jupyter notebooks. Pascal Bugnion demonstrates how to use Jupyter widgets to implement human-in-the-loop machine learning with highly interactive user interfaces.
| Talk Title | Human-in-the-loop data science with Jupyter widgets |
| Speakers | Pascal Bugnion (ASI Data Science) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | May 22-24, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Data science algorithms, in contrast to traditional software, are often nondeterministic. The correctness of an algorithm is much more subjective; therefore, being able to easily visualize intermediate stages in a data processing pipeline is tremendously important. Jupyter widgets allow for the creation of lightweight, interactive graphical interfaces directly in Jupyter notebooks. This provides the following advantages: graphical interfaces lead to a much shorter feedback loop for the data scientist, allowing them to rapidly experiment with their model; data scientists can use Python (rather than, say, JavaScript) to create user interfaces; the GUI is part of the pipeline, rather than a window onto it; and any output generated by the UI can be used for the next step of the pipeline. Pascal Bugnion demonstrates how to use Jupyter widgets to set GUIs up as part of the data science process. Join in to learn how to build human interaction into your data science process with as little friction as possible.
