When
Where
Webinar Materials
Michelle's slides - see Attachments below
GitHub repo
About the Webinar
Want to build an interactive web app to share your data or machine learning application? Join Michelle Yung, a software engineer at the UA Data Science Institute, to learn about Streamlit, a “low-code” solution that allows you to quickly build web applications by writing a python script. No web programming experience required! Michelle will walk us through how she trained a machine learning model and built the interactive web app for automatically detecting leaves from images using Streamlit.
Michelle and her collaborators used CyVerse to share and process data utilizing this application. Learn how you can incorporate Machine Learning into your analysis workflow. Streamlit is packed with features for all types of data — come learn how Streamlit can help you build a web application for whatever data you’re working with.
What You'll Learn
- How to use Streamlit, a low-code solution that allows you to quickly build web applications using Python scripts.
- How you can incorporate machine learning into your analysis workflow.
- No web programming experience needed!
About the Presenter
Michelle Yung currently is an Applications Developer for the UArizona's Data Science Institute and works in Data Management and Analysis Core (DMAC) of the UArizona's Superfund Research Program. The Program supports studies on the effects of mine tailings and arsenic on the surrounding environment and Michelle works closely with Superfund researchers to develop data pipelines and applications and to improve their data processes and management.