We have covered decent ground on using industry-grade cloud Deep Learning (DL) APIs in our applications in previous chapters and we have learned about their use through practical examples. In this chapter, we will cover a general outline for developing DL-enabled websites. This will require us to bring together all the things that we have learned so far so that we can put them to use in real-life use cases. In this chapter, we will learn how to structure a DL web application for production by first preparing the dataset. We will then train a DL model in Python and then wrap the DL models in APIs using Flask.
The following is a high-level summary of this chapter:
- Defining our problem statement
- Breaking the problem into several components
- Building a mental model to bind the project components
- How we should be collecting...
