Please use this identifier to cite or link to this item:
Title: MLModelCI : an automatic cloud platform for efficient MLaaS
Authors: Zhang, Huaizheng
Li, Yuanming
Huang, Yizheng
Wen, Yonggang
Yin, Jianxiong
Guan, Kyle
Keywords: Engineering::Computer science and engineering
Engineering::Computer science and engineering::Computing methodologies
Issue Date: 2020
Source: Zhang, H., Li, Y., Huang, Y., Wen, Y., Yin, J. & Guan, K. (2020). MLModelCI : an automatic cloud platform for efficient MLaaS. 28th ACM International Conference on Multimedia, 4453-4456.
Project: NRF2017EWT-EP003- 023
Abstract: MLModelCI provides multimedia researchers and developers with a one-stop platform for efficient machine learning (ML) services. The system leverages DevOps techniques to optimize, test, and manage models. It also containerizes and deploys these optimized and validated models as cloud services (MLaaS). In its essence, MLModelCI serves as a housekeeper to help users publish models. The models are first automatically converted to optimized formats for production purpose and then profiled under different settings (e.g., batch size and hardware). The profiling information can be used as guidelines for balancing the trade-off between performance and cost of MLaaS. Finally, the system dockerizes the models for ease of deployment to cloud environments. A key feature of MLModelCI is the implementation of a controller, which allows elastic evaluation which only utilizes idle workers while maintaining online service quality. Our system bridges the gap between current ML training and serving systems and thus free developers from manual and tedious work often associated with service deployment. We release the platform as an open-source project on GitHub under Apache 2.0 license, with the aim that it will facilitate and streamline more large-scale ML applications and research projects.
ISBN: 9781450379885
DOI: 10.1145/3394171.3414535
Rights: © 2020 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Page view(s)

Updated on Jan 16, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.