A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that! Yet to achieve product market fit and actually solve a problem
for customers, there’s a lot more that needs to happen beyond a giant corpus of data and the latest deep learning algorithm. So what happens when machine learning-as-a-service is really the feature... not the product? And what do you build, what do you buy, do you bother to customize?
listen to this episode of the a16z Podcast