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Observability for large language models : understanding and improving your use of LLMs /
An initial release of a large language model (LLM) makes for a nice marketing moment, but value lies in the work you do to make something a true "1.0"-level product experience. In this report, Phillip Carter, who spearheads AI initiatives at Honeycomb, provides an introduction to using obs...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc.,
2023.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a An initial release of a large language model (LLM) makes for a nice marketing moment, but value lies in the work you do to make something a true "1.0"-level product experience. In this report, Phillip Carter, who spearheads AI initiatives at Honeycomb, provides an introduction to using observability tools and practices that will help you improve modern LLM and AI products after they've been released. MLOps professionals, SREs, software engineers, developers, and architects will learn not only the importance of OpenTelemetry, but also the methods of feeding observability data back into development. This report is also ideal for CTOs and other senior-level practitioners in your organization. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Natural language processing (Computer science) | |
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| 650 | 0 | |a Observers (Control theory) | |
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