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F5 And NetApp Announces An Expanded Collaboration To Accelerate And Streamline Enterprise AI Capabilities Using Secure Multicloud Networking Solutions From F5 And NetApp's Suite Of Data Management Solutions

F5 And NetApp Announces An Expanded Collaboration To Accelerate And Streamline Enterprise AI Capabilities Using Secure Multicloud Networking Solutions From F5 And NetApp's Suite Of Data Management Solutions

F5和美國網存宣佈擴大合作,利用F5的安全多雲網絡解決方案和美國網存的數據管理解決方案,加速和簡化企業人工智能能力。
Benzinga ·  09/24 20:40

Collaboration bolsters generative AI capabilities with advanced data management and secure multicloud networking services for RAG integration

合作加強了先進的數據管理和安全的多雲網絡服務,爲RAG集成增強了生成式人工智能功能

F5 (NASDAQ:FFIV) and NetApp (NASDAQ:NTAP) today announced an expanded collaboration to accelerate and streamline enterprise AI capabilities using secure multicloud networking solutions from F5 and NetApp's suite of data management solutions. This collaboration leverages F5 Distributed Cloud Services to streamline the use of large language models (LLMs) across hybrid cloud environments. By integrating F5's secure multicloud networking with NetApp's data management, enterprises can implement Retrieval Augmented Generation (RAG) solutions efficiently and securely, enhancing the performance, security, and utility of their AI systems.

F5(納斯達克:FFIV)和美國網存(納斯達克:NTAP)今天宣佈擴大合作,加快並簡化企業人工智能能力的發展,使用F5和美國網存的數據管理解決方案套件中的安全的多雲網絡解決方案。 這一合作利用F5分佈式雲服務來簡化在混合雲環境中使用大型語言模型(LLMs)。 通過將F5的安全的多雲網絡與美國網存的數據管理整合,企業可以高效安全地實施檢索增強生成(RAG)解決方案,提升其人工智能系統的性能、安全性和效用。

As enterprises increasingly adopt AI technologies, the need for accurate and contextually relevant information becomes crucial—and that's where RAG comes in. RAG is an artificial intelligence technique that combines retrieval-based and generation-based approaches to improve the quality and relevance of responses by securely incorporating relevant, often proprietary, documents or information from a large dataset into the generated answers without exposing them to public large language models. RAG integrates the most recent, relevant data into AI responses. This ensures that AI models can deliver precise and context-specific answers.

隨着企業越來越多地採用人工智能技術,精準和具有相關上下文的信息需求變得至關重要,而這正是RAG的用武之地。 RAG是一種人工智能技術,它結合了檢索式和生成式方法,通過安全地將相關的、通常是專有的文檔或信息從大型數據集中合併到生成的答案中,從而改進響應的質量和相關性,而不會將其暴露給公共大型語言模型。 RAG將最新和最相關的數據集成到人工智能響應中。 這確保人工智能模型能夠提供精確和具有特定上下文的答案。

譯文內容由第三人軟體翻譯。


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