Introduction to Streamlit
If readers are familiar with Streamlit, they can move on to the Creating an application with Streamlit and AI agents section directly.
Companies have invested heavily in data science and AI. The models that are trained can guide business decisions and provide different insights. Training a model, using it, and extracting insights requires expertise that not everyone has. A model that is truly useful for a company must provide results that must then be used by other stakeholders as well. For example, when you train a model, it should generate results that are usable by other people. It is possible to create static visualizations of the data (exporting graphs), but they convey only limited information. One could provide information in a Jupyter notebook but not everyone is capable of using such a tool. One option that might allow easier access by others is to create a dashboard or web application.
This is where Streamlit comes in.
