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The Good, the Bad, and the Shiny: A Data Scientist's Guide to Choosing Python Web App Frameworks by Alan Feber
Presentation Slides: rstatsnyc.blob.core.windows.net/misc/The-Good-the-Bad-and-the-Shiny-A-Data-Scientists-Guide-to-Choosing-Python-Web-App-Frameworks_Alan-Feder.pdf
Abstract: Shiny has long been the go-to for data scientists creating web applications in R, but Python offers a plethora of alternatives that can be overwhelming to choose from. This talk will demonstrate the creation of a single application using three popular Python tools: PyShiny, Streamlit, and Gradio. We'll explore the strengths and weaknesses of each framework, through a live demonstration of each. This comparison will provide you with a practical roadmap for selecting the ideal Python web app framework to suit your specific data science projects.
Apps that were demoed during the talk:
-Streamlit (www.alanfeder.com/dcr_rag/streamlit_lite.html)
-Gradio (huggingface.co/spaces/AlanFeder/RGov_Gradio_App_-_Better_Chat)
-Shiny (www.alanfeder.com/dcr_rag/shinylive1/)
-HTML (www.alanfeder.com/dcr_rag/html_app.html)
Bio: Alan Feder has been a data scientist for over a decade, working with machine learning, AI, and statistics. He works as a Staff LLM Data Scientist for Magnifi by TIFIN, where he uses large language models to build an AI Copilot for investments. Alan also works as a freelance AI & educational consultant where he bridges the gap between state-of-the-art research and practical applications. Outside of work, Alan enjoys family time with his wife and two children in Potomac, MD.
Twitter: AlanFeder
Presented at the 2024 Government & Public Sector R Conference (October 30, 2024)
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