As far as I'm concerned, it is not about mutual exclusion. That is from the perspective of someone involved in building a machine learning platform[0] to allow

As far as I'm concerned, it is not about mutual exclusion. That is from the perspective of someone involved in building a machine learning platform[0] to allow users to use whatever they want to use in their experiments without being bothered with setting up their environnement, or differences in experiment tracking, or differences in deployment. We used to build custom products and while the multitude of frameworks has been useful, the fractures and fragmentation dealing with their peculiarities has hurt us. Now we build around differences to provide a cohesive experience. I think a useful way to look at these frameworks is different from the way we look at Web dev frameworks. You don't change framework every day in Web dev, but in machine learning, you want to experiment with as many things as necessary to get your results. TF, PyTorch, MXNet, Sklearn, XGBoost, whatever gets results. The problem is that this experimenting takes a toll and is painful. I believe that progress in a field is inversely proportional to the duration and cost of experiments. We're trying to reduce these to their systematic, irreducible, values. - [0]: https://iko.ai https://iko.ai