One of the best tools / libraries in the Data Science Arsenal.
Use Cases and Deployment Scope
Our Data Science team has built an entire multi-page reporting dashboard for the executive team to analyze all of their metrics (and we've started including machine learning models in the pages) in a really short amount of time. When a new report / metric page is needed, we can completely deliver (using Streamlit) in a day or two max. That's with authentication, logging, design, computing and testing.
We've also built a few integrations with access to our internal databases that allow a non-technical person to query a database themselves, in a simple manner and get answers they're looking for. One of our Customer Success agents wanted a joined file from multiple different databases and be able to download it whenever she wanted. Not knowing SQL or anything, she has a user interface that allows her to select checkboxes and build the query she wants without having any technical knowledge.
Pros
- Incredibly Easy
- Customizable
- Quick and powerful
Cons
- Recent Security issues (they quickly released an update to combat this though...)
- Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast.
Most Important Features
- Incredibly Easy to Use and Learn
- Gives end users an interface that's clean and doesn't require non-python work
- Renders pages & data really quickly.
- They've really thought through a ton of their features and provide help docs that are easy to get started.
Return on Investment
- I've scaled my team 2x since using Streamlit. We show off actual results that users can play with
- We're building a customer facing page that we're going to monetize.
- Incredible amounts of visibility into my team and what we're accomplishing.
Other Software Used
Google Analytics, Microsoft Power BI, Redpoint Global rgOne, Tableau Desktop, Slack