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...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Sebastopol, CA
O'Reilly Media, Inc.
2023
|
| Ausgabe: | First edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781098159757/?ar |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (33 Seiten) |
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| spelling | Carter, Phillip VerfasserIn aut Observability for large language models understanding and improving your use of LLMs Phillip Carter First edition. Sebastopol, CA O'Reilly Media, Inc. 2023 1 Online-Ressource (33 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Natural language processing (Computer science) Artificial intelligence Observers (Control theory) Traitement automatique des langues naturelles Intelligence artificielle Observabilité (Théorie de la commande) artificial intelligence Artificial intelligence (OCoLC)fst00817247 Natural language processing (Computer science) (OCoLC)fst01034365 Observers (Control theory) (OCoLC)fst01042959 |
| spellingShingle | Carter, Phillip Observability for large language models understanding and improving your use of LLMs Natural language processing (Computer science) Artificial intelligence Observers (Control theory) Traitement automatique des langues naturelles Intelligence artificielle Observabilité (Théorie de la commande) artificial intelligence Artificial intelligence (OCoLC)fst00817247 Natural language processing (Computer science) (OCoLC)fst01034365 Observers (Control theory) (OCoLC)fst01042959 |
| subject_GND | (OCoLC)fst00817247 (OCoLC)fst01034365 (OCoLC)fst01042959 |
| title | Observability for large language models understanding and improving your use of LLMs |
| title_auth | Observability for large language models understanding and improving your use of LLMs |
| title_exact_search | Observability for large language models understanding and improving your use of LLMs |
| title_full | Observability for large language models understanding and improving your use of LLMs Phillip Carter |
| title_fullStr | Observability for large language models understanding and improving your use of LLMs Phillip Carter |
| title_full_unstemmed | Observability for large language models understanding and improving your use of LLMs Phillip Carter |
| title_short | Observability for large language models |
| title_sort | observability for large language models understanding and improving your use of llms |
| title_sub | understanding and improving your use of LLMs |
| topic | Natural language processing (Computer science) Artificial intelligence Observers (Control theory) Traitement automatique des langues naturelles Intelligence artificielle Observabilité (Théorie de la commande) artificial intelligence Artificial intelligence (OCoLC)fst00817247 Natural language processing (Computer science) (OCoLC)fst01034365 Observers (Control theory) (OCoLC)fst01042959 |
| topic_facet | Natural language processing (Computer science) Artificial intelligence Observers (Control theory) Traitement automatique des langues naturelles Intelligence artificielle Observabilité (Théorie de la commande) artificial intelligence |
| work_keys_str_mv | AT carterphillip observabilityforlargelanguagemodelsunderstandingandimprovingyouruseofllms |