In SWE, there’s an underlying assumption that code and data are separated. In fact, in SWE, we want to keep things as modular and separate as possible (see the Wikipedia page on separation of concerns). On the contrary, ML systems are part code, part data, and part artifacts created from the two. The trend in the last decade shows that applications developed with the most/best data win. Instead of focusing on improving ML algorithms, most companies will focus on improving their data. Because data can change quickly, ML applications need to be adaptive to the changing environment, which might
...more
