Designing and Implementing Large-Scale, Robust ML Software
So far, we have learned how to develop ML models, how to work with data, and how to create and test the entire ML pipeline. What remains is to learn how we can integrate these elements into a user interface (UI) and how to deploy it so that they can be used without the need to program. To do so, we’ll learn how to deploy the model complete with a UI and the data storage for the model.
In this chapter, we’ll learn how to integrate the ML model with a graphical UI programmed in Gradio and storage in a database. We’ll use two examples of ML pipelines – an example of the model for predicting defects from our previous chapters and a generative AI model to create pictures from a natural language prompt.
In this chapter, we’re going to cover the following main topics:
- ML is not alone – elements of a deployed ML-based system
- The UI of an ML model
- Data storage
- Deploying...
