2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2017.192
Deep learning has improved the state-of-the-art results in many domains, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of lifecycle management are largely ignored. Deep learning modeling lifecycle contains a rich set of artifacts and frequently conducted tasks, dealing with them is cumbersome and left to the users. To address these issues in a comprehensive manner, we demonstrate ModelHub, which includes a novel model versioning system (dlv), a domain-specific language for searching through model space (DQL), and a hosted service (ModelHub). Video: https://youtu.be/4JVehm5Ohg4.
Machine learning, Predictive models, Training, Computational modeling, Metadata, Tuning
H. Miao, A. Li, L. S. Davis and A. Deshpande, "ModelHub: Deep Learning Lifecycle Management," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 1393-1394.