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Operationalizing AI /
Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capa...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2021.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capabilities into their products and workflows, but they often think the data science team alone can lead the change. This report examines issues from several conversations the authors held with data science teams across industries, as well as those issues they've witnessed in their own experience as builders and leaders. Among their findings, the authors agreed that to shorten the production process, lower overhead, and reduce risk, organizations need a comprehensive understanding of how to build AI in a repeatable fashion. Technologists John J. Thomas, Paco Nathan, and William Roberts show data scientists how an organization and its technology work together to support their mission. Leaders of data science teams will examine how their organizations can transparently and seamlessly facilitate the delivery of data products. And business leaders will learn the value, both realized and potential, of introducing data science expertise in their organizations. | ||
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| 700 | 1 | |a Nathan, Paco, |e author. | |
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