Year: 2022
Type: conference-paper
Abstract: DocuSign is advancing at a great pace for artificial intelligence and embracing a continuous shift towards developing and deploying an increasing number of deep learning models. During the development stage, developers usually build a number of deep learning models and train them using a bunch of potential hyperparameter configurations to find the best-performed one, which is called hyperparameter optimization (HPO). Such HPO jobs can run for a long time due to ever-larger models and numerous hyperparameter configurations. Furthermore, the HPO jobs at DocuSign are processed in container-based environments so that the best-performed model can be deployed and maintained in production reliably and efficiently.
Authors: Rui Liu, David Wong, Dave Lange, Patrik Larsson, Vinay Jethava, Qing Zheng
Institutions: University of Chicago, Docusign (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4281389450