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Executive briefing : why machine-learned models crash and burn in production and what to do about it /
"Much progress has been made over the past decade on process and tooling for managing large-scale, multi-tier cloud apps and APIs, but there is far less common knowledge on best practices for managing machine-learned models (classifiers, forecasters, etc.), especially beyond the modeling, optim...
| Clasificación: | Libro Electrónico |
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "Much progress has been made over the past decade on process and tooling for managing large-scale, multi-tier cloud apps and APIs, but there is far less common knowledge on best practices for managing machine-learned models (classifiers, forecasters, etc.), especially beyond the modeling, optimization, and deployment process once these models are in production. A key mindset shift required to address these issues is understanding that model development is different than software development in fundamental ways. David Talby (Pacific AI) shares real-world case studies showing why this is true and explains what you can do about it, covering key best practices that executives, solution architects, and delivery teams must take into account when committing to successfully deliver and operate data science-intensive systems in the real world. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page |
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| Notas: | Title from title screen (viewed January 20, 2020). |
| Descripción Física: | 1 online resource (1 streaming video file (37 min., 20 sec.)) |