Harikaran Subbaraj
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2020-75
May 28, 2020
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.pdf
The need for systems to support machine learning inference has grown as the im- portance of machine learning in production systems has increased. Serving pipelines of machine learning models comes with challenges of scaling, low-latency require- ments for requests, and high computation costs for different stages of the pipeline. In this paper we propose that by taking a dataflow abstraction we can simplify and increase performance of serving these machine learning pipelines. The proposed system FLOWSERVEcombines this dataflow paradigm with Cloudburst, a stateful function as a service (FaaS) system to provide a framework to deploy and serve machine learning pipelines at scale. We provide several logical and physical opti- mizations that make FLOWSERVEoutperform currently used research and industry systems.
Advisor: Joseph Gonzalez
BibTeX citation:
@mastersthesis{Subbaraj:EECS-2020-75, Author = {Subbaraj, Harikaran}, Title = {Using Dataflow for Machine Learning Inference}, School = {EECS Department, University of California, Berkeley}, Year = {2020}, Month = {May}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.html}, Number = {UCB/EECS-2020-75}, Abstract = {The need for systems to support machine learning inference has grown as the im- portance of machine learning in production systems has increased. Serving pipelines of machine learning models comes with challenges of scaling, low-latency require- ments for requests, and high computation costs for different stages of the pipeline. In this paper we propose that by taking a dataflow abstraction we can simplify and increase performance of serving these machine learning pipelines. The proposed system FLOWSERVEcombines this dataflow paradigm with Cloudburst, a stateful function as a service (FaaS) system to provide a framework to deploy and serve machine learning pipelines at scale. We provide several logical and physical opti- mizations that make FLOWSERVEoutperform currently used research and industry systems.} }
EndNote citation:
%0 Thesis %A Subbaraj, Harikaran %T Using Dataflow for Machine Learning Inference %I EECS Department, University of California, Berkeley %D 2020 %8 May 28 %@ UCB/EECS-2020-75 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.html %F Subbaraj:EECS-2020-75