Is There a Revolution in Scalable Algorithms?

Before you had machine learning, algaritms that you basically had to implement from scratch or they were really, really difficult to get them up in running at scale. Now it's not only that, you don't have to implement them from scratch, right? There are robust communities across each of these entl frameworks. The next things, i think, are visualization and explainability. So for linear algorithms, common thing you can do is just look at regression coesitions. When it comes to non linear methods, it's much, much harder to say what the actual impact of a given feature is on a specific prediction. But now we have some very powerful explainability methods.