Standardizing a Machine Learning Framework for Applied Research

Until now, the Machine Learning (ML) frameworks we've used at Borealis AI have varied according to individual preference. But as our applied team grows, we're finding that a preference-based system has certain shortcomings that have led to inefficiencies and delays in our research projects. As a result, we identified two main arguments in favour of standardizing a single framework for the lab. It has been our experience that independent frameworks do not often "play well" together. For example, a TensorFlow-based model applied to one research project would have to be rewritten in PyTorch for another project.