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Automating DevOps for machine learning

"Learning common deployment architectures for machine learning in the real world and understanding how to build your model for a production environment can help you avoid pitfalls when scaling up. Diego Oppenheimer (Algorithmia) discusses common problems and solutions and shares best practices...

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Bibliographic Details
Main Author: Oppenheimer, Diego
Format: eBook
Language:English
Published: [Place of publication not identified] O'Reilly Media 2019
Subjects:
Computer Software / Development / Http://id.loc.gov/authorities/subjects/sh85029535
Machine Learning / Http://id.loc.gov/authorities/subjects/sh85079324
Software Architecture / Http://id.loc.gov/authorities/subjects/sh2004001071
Apprentissage Automatique
Architecture Logicielle
Software Architecture / Fast / (ocolc)fst01200416
Computer Software / Development / Fast / (ocolc)fst00872537
Machine Learning / Fast / (ocolc)fst01004795
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Description
Summary:"Learning common deployment architectures for machine learning in the real world and understanding how to build your model for a production environment can help you avoid pitfalls when scaling up. Diego Oppenheimer (Algorithmia) discusses common problems and solutions and shares best practices from leading organizations that have solved the deployment headache. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page
Item Description:Title from resource description page (Safari, viewed January 31, 2020)
Physical Description:1 streaming video file (40 min., 10 sec.)

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