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AI and Analytics at Scale
Getting large-scale data-driven applications for AI and analytics into production doesn't have to be challenging. Technical managers, senior technologists, and implementers today often overlook fundamental aspects of design and data infrastructure--aspects that can make the difference between f...
| Autores principales: | , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2021
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Getting large-scale data-driven applications for AI and analytics into production doesn't have to be challenging. Technical managers, senior technologists, and implementers today often overlook fundamental aspects of design and data infrastructure--aspects that can make the difference between failed approaches and reliable, successful production systems. In this exclusive report, you'll learn which practices work--and which don't--at large and innovative companies that have successfully integrated AI and analytics into their workflows. Over the past two years, authors Ted Dunning and Ellen Friedman have worked with a wide range of businesses to deliver in-production systems at a large scale. You'll learn practices that have been particularly beneficial, including many that have been disregarded. Understand why AI is at its best when coupled with analytics Build successful production systems--running AI and analytics on the same infrastructure--at scale with less effort, pressure, and cost Apply aspects of a scale-efficient system, including a comprehensive data strategy, containerization, and scalability without scaling IT Focus on the increasingly popular topics of AI and edge computing Explore an example data infrastructure: HPE Ezmeral Data Fabric. | ||
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