Complex Machine Learning Workflows at Scale

Uber's Michelangelo is a machine learning platform that supports training and serving thousands of models in production. In this talk, we will describe how Michelangelo evolved its architecture to handle more complex machine learning workflows e.g. model ensembling.
Who is this presentation for?
Software Engineers, ML/Research Engineers, Data Scientists
Prerequisite knowledge:
General ML, Spark
What you'll learn?

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.