In this section, we will talk about the security measures, monitoring techniques, and performance optimizations that can be integrated into a DL solution in production. These functionalities are essential to maintaining solutions that depend on AI backends. While we have discussed the security methods facilitated by DL in previous chapters, we will discuss the possible security threats that could be posed to an AI backend.
One of the largest security threats to AI backends is from noisy data. In most of the methodologies for having AI in production, it is important to regularly check for new types of noise in the dataset that it is trained on.
Here is a very important message for all developers who love the Python pickle library:
The preceding screenshot is taken from the official Python documentation at...
