Summary

In this chapter, we explored an example costing analysis, which showed us a job-specific use case for Streamlit. In this example, we discussed how to use interactive Streamlit applications to help improve and inform the data-based decision making of teams. After that, we also learned how to deploy Streamlit applications from private GitHub repositories, and we learned about multiple methods to make our Streamlit applications only available to a private audience with password protection and Google SSO. This concludes the chapter.

In the next chapter, we will focus on interviews with power Streamlit users and creators to learn tips and tricks, why they use Streamlit so extensively, and also where they think the library will go from here. See you there!