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...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc
2023.
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| Edición: | First edition |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009768137906719 |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (33 pages) |
| ISBN: | 9781098159757 |
