Deep Learning AI Needs Tools To Adapt To Changes In The Data Environment

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.