With the advent of so many computing and serving frameworks, it is getting stressful day by day for the developers to put a model into production . If the question of what model performs best on my data was not enough, now the question is what framework to choose for serving a model trained with Sklearn or LightGBM or PyTorch . And new frameworks are being added as each day passes.
So is it imperative for a Data Scientist to learn a different framework because a Data Engineer is comfortable with that, or conversely, does a Data Engineer need to learn a new platform that the Data Scientist favors?
Add to that the factor of speed and performance that these various frameworks offer, and the question suddenly becomes even more complicated.
So, I was pleasantly surprised when I came across the Hummingbird project on Github recently, which aims to answer this question or at least takes a positive step in the right direction.
