Deploying machine learning models

Bringing into production a project based on machine learning isn't easy. In fact, there are only a few companies that have managed to do it, at least for large projects. The difficulties lie in the fact that artificial intelligence is not something that is produced with finished software. A starting platform is needed to implement its own software model encountering problems that are not analogous to those that the developers usually encounter. The classic approach of software engineering leads to abstraction so that you arrive at simple code that can be modified and improved. Unfortunately, it is difficult to pursue abstraction in machine learning applications, just as it is difficult to control the complexity of machine learning. The best thing to do is focus on a platform that has the functions you need and, at the same time, allows you...