The global Call for Code is well underway, we want to share some visual recognition models which could help you. These AI models can operate on the edge, which could be particularly useful for this years’ theme: disaster preparedness. How could visual recognition help in relief work? From satellite and drone imagery analysis, to classifying...
Built for anyone using data to create Jupyter Notebooks and other artifacts, this journey shows the power of the open source helper library PixieDust. With PixieDust, hosted on IBM Watson Studio, a developer or other user can quickly create charts, graphs, and tables without complex code, in an interactive and dynamic manner. In addition, PixieApps are used to embed UI elements directly in the Jupyter Notebook. Given an open source data provider like the city of San Francisco’s DataSF Open Data, PixieDust and IBM Watson Studio can empower the user to analyze and share data visualizations.
DataSF Open Data provides hundreds of data sets from the city and county of San Francisco. In this pattern, we demonstrate how to incorporate open data into a Jupyter Notebook hosted on IBM Watson Studio, and how to quickly and easily create graphs and charts using PixieDust. We then use PixieApps to create UI elements that can be run directly in the Jupyter Notebook.
When you have completed this journey, you will understand how to:
- Load the provided notebook onto the IBM Watson Studio platform.
- DataSF Open Data traffic info is loaded into the Jupyter Notebook.
- The notebook analyzes the traffic info.
- You can interactively change charts and graphs.
- A PixieApp dashboard is created and can be interacted with.