Machine learning pipelines are brittle and hard to debug as the data representation is condensed to numbers in matrices instead of expressive data structures with semantically meaningful variable names.

To mitigate this complexity, PyTorch and Tensorflow are heavy weight machine learning frameworks that give your software a lot of structure as well as tooling to monitor the progress of training and debug your models as well as some deployment tooling.

Any neural net based component of software will likely be developed in one of these frameworks.