Keynotes page

The process of developing machine learning (ML)-based systems is in many aspects different from the process of developing conventional software systems, where the accumulated knowledge on Software Engineering (SE) can guide. In order to facilitate the discussions on what existing SE practices can be applied and what are missing for ML-based systems, we launched a community on Machine Learning Systems Engineering in 2017. Our activities cover a wide range of aspects regarding developing and operating ML-based systems, from requirement development, testing and quality assurance, tools and computing infrastructure, to operating and management issues. This talk reflects on the five year activities, reviews the original goals and what we have achieved and what we have not, and discuss the future directions.

Dr. Hiroshi Maruyama has spent 26 years in IBM Research, Tokyo Research Laboratory, working on various computer science areas such as artificial intelligence, natural language processing, machine translation, hand-writing recognition, multimedia, XML, Web Services, and security. He was the director of IBM Tokyo Research Laboratory from 2006 to 2009. From 2011 to 2016, he was a professor at the Institute of Statistical Mathematics where he worked on projects related to big data, statistics, and their impacts on society. He joined Preferred Networks, Inc. in April 2016 as the chief strategy officer. His current research interests include practical applications of machine learning, social implications of information technology and machine learning, and computer science and statistics in general. Currently he is an Executive Fellow at Kao Corporation, a PFN Fellow at Preferred Networks, and a project professor at the Research into Artifacts, Center for Engineering at the University of Tokyo.