Handling New Data
Models can be trained once using a set of data and can then be used to make predictions. Such static models can be very useful, but it is often the case that we want our model to continuously learn from new data—and to continuously get better as it does so.
In this section, we will discuss two strategies of handling new data and see how to implement them in Python.
Separating Data and Model
When building a deep learning application, the two most important areas are data and model. From an architectural point of view, it is recommended that these two areas be kept separate. We believe that is a good suggestion because each of these areas includes functions inherently separate from each other. Data is often required to be collected, cleaned, organized, and normalized, whereas models need to be trained, evaluated, and able to make predictions.
Following that suggestion, we will be using two different code bases to help us build our web application...
