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2019-04-24T14:00:35.554-04:00
Microsoft® Word for Office 365
Jared Dean
00c2264d11a7ba1ffca5dd1c59cf91f33179a904
438129
SAS MAS Python Flask FACTMAC recommendation API terraform OpenStack cloud
Microsoft® Word for Office 365
<p>In the excitement and hype around machine learning (ML) and artificial intelligence (AI), most of the time is spent on the model building. Much less energy is expended on how to take the insights from models and deploy them efficiently to create value and improve business outcomes. This paper shows a complete example using DevOps principals for building models and deploying them using SAS® in conjunction with opens source projects including Docker, Flask, Jenkins, Jupyter, and Python. The reference application is a recommendation engine on a web property with a global user base. This use case forces us to confront security, latency, scalability, and repeatability. The paper outlines the final solution but also includes some of the problems encountered along the way that informed the final solution.</p>
<p>Jared Dean, SAS</p>
<p style="font-family: Arial,Helvetica,sans-serif; font-size: 90%; line-height: 300%; margin: -3em 0em 2em 0em;"><a href="https://github.com/sascommunities/sas-global-forum-2019">Access sample code</a></p>
Session 3368
en
jeff
<p>In the excitement and hype around machine learning (ML) and artificial intelligence (AI), most of the time is spent on the model building. Much less energy is expended on how to take the insights from models and deploy them efficiently to create value and improve business outcomes. This paper shows a complete example using DevOps principals for building models and deploying them using SAS® in conjunction with opens source projects including Docker, Flask, Jenkins, Jupyter, and Python. The reference application is a recommendation engine on a web property with a global user base. This use case forces us to confront security, latency, scalability, and repeatability. The paper outlines the final solution but also includes some of the problems encountered along the way that informed the final solution.<br>
</p>
<p><a href="https://github.com/sascommunities/sas-global-forum-2019">Access sample code files now</a></p>
<p>Jared Dean, SAS</p>
Microsoft® Word for Office 365
2019-04-12T11:55:36.000-04:00
2019-04-12T11:55:36.000-04:00
2019-04-10T08:53:51.000-04:00
application/pdf
SAS MAS Python Flask FACTMAC recommendation API terraform OpenStack cloud
2019-04-24T14:00:35.462-04:00
Jared Dean
In the excitement and hype around machine learning (ML) and artificial intelligence (AI), most of the time is spent on the model building. Much less energy is expended on how to take the insights from models and deploy them efficiently to create value and improve business outcomes. This paper shows a complete example using DevOps principals for building models and deploying them using SAS® in conjunction with opens source projects including Docker, Flask, Jenkins, Jupyter, and Python. The reference application is a recommendation engine on a web property with a global user base. This use case forces us to confront security, latency, scalability, and repeatability. The paper outlines the final solution but also includes some of the problems encountered along the way that informed the final solution.
Access sample code files now
Jared Dean, SAS
Deploying Models Using SAS® and Open Source
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industry:5850
support:skill-level/advanced
support:sgf-papers/topic/programming/open-integration
support:customer-roles/data-scientist
support:sgf-papers
software:BASE
year:2019
event-type:180/session-type/breakout
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