Often machine learning training and production deployment of machine learning have been two different and distinct challenges. Data Scientist do not often consider the constraints of production deployment and Engineers are weary of debugging often lengthy code base that go along with models. Over the years, Azure has developed tools to minimize the gap between these two processes – allowing Data Scientist to focus on machine learning and Engineers to interface cleanly without having to inherit large code bases. In this session we will cover how these Azure tools accelerated our machine learning development and how these tools have changed over the years to help us move even faster.
Jeffrey Wu, is a data visionary experienced data scientist and technologist with a track record of driving key product development that fuel company growth across various B2C and B2B industries. Jeff excels at identifying and visualizing complex data trends, building back-end infrastructure for sophisticated data models and structures, and has a knack for communicating technical details to non-technical audiences. Jeff has designed, developed, and managed numerous data models and data processing architectures that are efficient, scalable, and drive core business results. Jeff holds a B.A. in Applied Mathematics from the University of California, Berkeley.