Automating DevOps for machine learning

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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Detalles Bibliográficos
Autor Corporativo: O'Reilly Strata Data Conference (-)
Otros Autores: Oppenheimer, Diego, onscreen presenter (onscreen presenter)
Formato: Vídeo online
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly Media 2019.
Materias:
Machine learning.
Software architecture.
Computer software > Development.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822775706719
Descripción
Sumario:"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.
Notas:Title from resource description page (Safari, viewed January 31, 2020).
Descripción Física:1 online resource (1 streaming video file (40 min., 10 sec.)) : digital, sound, color

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