Composable Machine Learning
Composable Machine Learning


Machine Learning systems for complex tasks - such as controlling industrial manufacturing processes in real-time; writing x-ray medical reports; and cognitive chatbots - simply cannot be downloaded off an "algorithm marketplace" or model repository. Rather, they must be built from first principles - yet realistically, most ML teams do not have the advanced skills necessary to do this. Even teams with experts may end up with hand-crafted, one-off solutions that are difficult to scale into production. To break this status quo, ML systems need to become composable, so that ML teams can build applications for a richer spectrum of AI tasks from standardized and reusable building blocks, and take them into scalable production. Texar is such a composable ML development tool developed at Petuum, that shortens the ML development cycle by allowing developers to assemble complex ML systems in a symbolic manner. At Petuum, we use Texar to create sophisticated and original ML systems such as medical report generators for chest x-ray images, multi-lingual (English, Chinese and Japanese) cognitive chatbots for retail in-store assistance and call center support, as well as reproduce and extend recent models from the research community such as BERT. Petuum offers Texar as open source under a friendly license, and we hope that it can benefit other ML teams searching for a sustainable way to produce the next generation of AI applications.


Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.

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