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将 GenAI 推理从原型扩展到生产:速度与成本的现实经验.pdf

上传人: Fl****zo 编号:718900 2025-06-22 36页 2.45MB

1、Forward-looking StatementThis presentation has been prepared for informational purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the date of this presentation unless stated otherwise.This pres

2、entation and the accompanying oral commentary may contain forward-looking statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“believes”,“estimates”,“predicts”,or“continue”,or the neg

3、ative of these words or other similar terms or expressions that concern Databricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently subject to risks and uncertainties that could cause actu

4、al results to differ materially from those expressed in or suggested by the forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not undertake any obligation to publicly update or revise an

5、y forward-looking statement,whether as a result of new information,future developments or otherwise.3Scaling GenAI InferencePrototype to ProductionAnish KumarJune 10,2025Scribd Inc.Scribd,Everand,SlideshareHow it startedThe problemMetadata ExtractionExtract rich structured metadata from Ebooks and A

6、udiobooksThe bigger pictureEnriching documents using GenAI inferenceUnique challenges150,000 audiobooks500,000 ebooksMultiple languagesRealtime Whisper(audio transcription)Evaluate LLMsCostQualityDue to size and type of corpusLLM evaluations10From recent projectsPrompt Engineering and evaluation20 a

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本文主要讲述了Scribd公司如何使用Databricks平台,将生成式人工智能(GenAI)推断原型扩展到生产环境。关键点如下: 1. **挑战**:处理15万本有声书和50万本电子书,涉及多种语言,需要实时语音转录和大规模语言模型(LLM)评估。 2. **问题**:传统方法(如Spark)在处理大量数据时速度慢且成本高。 3. **解决方案**:采用工作流和批量推断,优化了输入数据处理、API调用并发控制,并使用无服务器计算进行实时推断。 4. **核心数据**:完成了40亿张图像分析、1000万文档嵌入、1亿文档文本提取和50万小时语音转录。 5. **改进**:从项目启动到完成的时间缩短至约2周,成本估算在几天内完成,且高概率无需人工干预。 6. **招聘**:文章最后提到公司正在招聘,涉及AI、多模态、多语言等领域的挑战性问题。 通过这些措施,Scribd提高了处理效率,降低了成本,并实现了流程的自动化和系统化。
"如何快速提取电子书元数据?" "生产环境中AI推断的挑战有哪些?" "Scribd如何优化大规模文档处理?"
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