Supercharge Your Shallow ML Models With Hummingbird

Since the most recent resurgence of deep learning in 2012, a lion's share of new ML libraries and frameworks have been created. The ones that have stood the test of time (PyTorch, Tensorflow, ONNX, etc) are backed by massive corporations, and likely aren't going away anytime soon. This also presents a problem, however, as the deep learning community has diverged from popular traditional ML software libraries like scikit-learn, XGBoost, and LightGBM. When it comes time for companies to bring multiple models with different software and hardware assumptions into production, things get…hairy. Using microservices in Kubernetes can solve the design pattern issue to an extent by keeping things de-coupled…if that's even what you want?