For a little bit of background, I've been studying stochastic calc and a few of it's applications (currently I'm still at the early stages of learning applications) and have been curious as to whether or not trading strategies using stochastic modeling are still relevant in the modern day age (late 2017 as I'm writing). One example might be: seeing as the familiarity with Black-Scholes has grown so much over that past ~30 years, using it as a strategy to find and capitalize off of what a trader might deem a mis-pricing no longer seems do-able (or at best, has become extremely difficult) due to it's popularity (i.e. since everybody has been trained on how to apply it/where to use it, hence it's become less effective at doing what it was able to do in the past).
My main questions are as follows: has the evolution of tools in Machine Learning replaced the role of model building using stochastic equations (can more complex models built from ML perform the same role as stochastic modeling without the uniformity of logic/formulae/precise derivations of models)? What is the main role of present day stochastic modeling in finance? Are there any hybrid stochastic machine learning models/methodologies? What role will stochastic calc/modeling play in finance within the next 10 years or so?