August 2021
Beginner
282 pages
5h 36m
English
Content preview from Getting Started with Streamlit for Data Science
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Chapter 4: Using Machine Learning with Streamlit
A very common situation data scientists find themselves in is at the end of the model creation process, not knowing exactly how to convince non-data scientists that their model is worthwhile. They might have performance metrics from their model or some static visualizations but have no easy way to allow others to interact with their model.
Before Streamlit, there were a couple of other options, the most popular being creating a full-fledged app in Flask or Django or turning their model into an Application Programming Interface (API) and pointing developers toward it. These are great options but tend to be time-consuming and suboptimal for valuable use cases such as prototyping an app.
The incentives ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.