The standard ML workflow
The first step to creating an app that uses ML is the ML model itself. There are dozens of popular workflows for creating your own ML models. It's likely you might have your own already! There are two parts of this process to consider:
- The generation of the ML model
- The use of the ML model in production
If the plan is to train a model once and then use this model in our Streamlit app, the best method is to create this model outside of Streamlit (for example, in a Jupyter notebook or in a standard Python file) first, and then use this model within the app.
If the plan is to use the user input to train the model inside our app, then we can no longer create the model outside of Streamlit and instead will need to run the model training within the Streamlit app.
We will start by building our ML models outside of Streamlit and move on to training our models inside of Streamlit apps after.
