Desiderata for next generation of ML model serving
Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This position paper puts forth a range of important qualities that next generation of inference platforms should be aiming f...
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creator | Akoush, Sherif Paleyes, Andrei Van Looveren, Arnaud Cox, Clive |
description | Inference is a significant part of ML software infrastructure. Despite the
variety of inference frameworks available, the field as a whole can be
considered in its early days. This position paper puts forth a range of
important qualities that next generation of inference platforms should be
aiming for. We present our rationale for the importance of each quality, and
discuss ways to achieve it in practice. We propose to focus on data-centricity
as the overarching design pattern which enables smarter ML system deployment
and operation at scale. |
doi_str_mv | 10.48550/arxiv.2210.14665 |
format | Article |
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variety of inference frameworks available, the field as a whole can be
considered in its early days. This position paper puts forth a range of
important qualities that next generation of inference platforms should be
aiming for. We present our rationale for the importance of each quality, and
discuss ways to achieve it in practice. We propose to focus on data-centricity
as the overarching design pattern which enables smarter ML system deployment
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variety of inference frameworks available, the field as a whole can be
considered in its early days. This position paper puts forth a range of
important qualities that next generation of inference platforms should be
aiming for. We present our rationale for the importance of each quality, and
discuss ways to achieve it in practice. We propose to focus on data-centricity
as the overarching design pattern which enables smarter ML system deployment
and operation at scale.</description><subject>Computer Science - Learning</subject><subject>Computer Science - Software Engineering</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotjs1qAjEURrPpolgfoKvmBUZvfm7MLIu2Kox04364JjcS0JmSEbFvX_9WH5wPDkeIdwUT6xFhSuWSzxOtr0BZ5_BV6AUPOXKhE8nUF9nx5ST33N1I7jvZJ7lp5LGPfJADl3Pu9m_iJdFh4PFzR2L7_bWdr6rmZ7mefzYVuRlWDtgxzbwJmvTOBUgeYjIhUrreBMGhR1BYg935qDCwBaOTsnWNYDmZkfh4aO_R7W_JRyp_7S2-vcebf5HKPf8</recordid><startdate>20221026</startdate><enddate>20221026</enddate><creator>Akoush, Sherif</creator><creator>Paleyes, Andrei</creator><creator>Van Looveren, Arnaud</creator><creator>Cox, Clive</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20221026</creationdate><title>Desiderata for next generation of ML model serving</title><author>Akoush, Sherif ; Paleyes, Andrei ; Van Looveren, Arnaud ; Cox, Clive</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-60e6ea783c2a2b6c0f80df3cdaf675a0c6585015904b8d15ce4032f1499504ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Learning</topic><topic>Computer Science - Software Engineering</topic><toplevel>online_resources</toplevel><creatorcontrib>Akoush, Sherif</creatorcontrib><creatorcontrib>Paleyes, Andrei</creatorcontrib><creatorcontrib>Van Looveren, Arnaud</creatorcontrib><creatorcontrib>Cox, Clive</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Akoush, Sherif</au><au>Paleyes, Andrei</au><au>Van Looveren, Arnaud</au><au>Cox, Clive</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Desiderata for next generation of ML model serving</atitle><date>2022-10-26</date><risdate>2022</risdate><abstract>Inference is a significant part of ML software infrastructure. Despite the
variety of inference frameworks available, the field as a whole can be
considered in its early days. This position paper puts forth a range of
important qualities that next generation of inference platforms should be
aiming for. We present our rationale for the importance of each quality, and
discuss ways to achieve it in practice. We propose to focus on data-centricity
as the overarching design pattern which enables smarter ML system deployment
and operation at scale.</abstract><doi>10.48550/arxiv.2210.14665</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Learning Computer Science - Software Engineering |
title | Desiderata for next generation of ML model serving |
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