Michael Mui and Kai Fricke discuss XGBoost-Ray, a scalable backend for distributed XGBoost training.
Michael is a software engineer on the Michelangelo machine learning platform team at Uber. Michael enjoys applying techniques from optimization and building large-scale distributed systems to solve difficult decision problems. Prior to Uber, he worked at Samsung Research on sensor fusion and probabilistic state estimation. Michael received a B.S in Electrical Engineering and Computer Science (EECS) from Berkeley.
Kai is a software engineer at Anyscale, the company behind the distributed computing platform Ray. He mostly works on developing machine learning libraries on top of Ray, like Ray Tune or RaySGD.