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使用 Databricks、MosiacML 和 MLRun 构建您的第一个 GenAI 应用程序.pdf

上传人: 张** 编号:167680 2024-06-15 19页 2.30MB

1、2024 Databricks Inc.All rights reservedBuilding your First gen AI App using Databricks,MosiacML and MLRunYaron HavivYaron Haviv,Co-Founder&CTO,Iguazio(acquired by McKinsey&Company)Bruce PhilpBruce Philp,Partner,Global Head of Data Engineering,QuantumBlack,AI by McKinsey12024 Databricks Inc.All right

2、s reserved2Meet our speakersBruce PhilpPartner Global Head of Data Engineering,QuantumBlack,McKinseys AI armYaron HavivCo-Founder&CTO,Iguazio(acquired by McKinsey&Company)2024 Databricks Inc.All rights reserved3Developing a gen AI PoC is straightforward,advancing to production can be extremely chall

3、enging,particularly for complex use-casesExamples of useCustomer Service Smart AnalysisGenAI real-time co-pilot(For sales up-lift)HR Support(Automation of HR processes)Legal Contract GenerationAssisted Code CreationCreative Content GenerationExamples of use6 months2 weeksPoCProductionBasic use-cases

4、Implementing a simple or small-scale use case can be managed by leveraging prompt engineering on top of a hosted commercial LLM,such as OpenAIComplex use-casesMoving complex or enterprise-scale use cases to production requires a compatible architecture,effective resource monitoring and optimization,

5、alongside a new operating model with built-in risk controls to ensure enterprise-grade performance,cost and risk profile2024 Databricks Inc.All rights reserved4Only a handful of organizations benefit from gen AI in production at scaleKey challenges of productionizing and scaling GenAI use casesTech

6、infrastructure not flexible enough(e.g.,complex use cases with mix of structured and unstructured data)Insufficient automationLittle standardizationLong time to marketLegal,Regulatory and Reputational riskssignificantly increase,driven by Intellectual Property,Privacy,Toxicity,“Hallucination”/mis-in

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本文主要探讨了构建和生产第一代人工智能应用程序的过程,重点关注了使用Databricks、MosiacML和MLRun的相关技术和框架。文章指出,尽管原型设计相对简单,但将AI应用程序推向生产环境却极具挑战性,特别是对于复杂的用例。文中提到了一些关键挑战,包括技术基础设施不够灵活、自动化程度不足、缺乏标准化、市场时间过长、法律和合规风险增加等。文章还讨论了如何通过有效的数据管理、模型测试和验证、以及构建健壮的生产管道等措施来降低风险。同时,文章强调了保护大型语言模型(LLM)免受风险的重要性,并提出了“权利、限制、伤害和自由”(RLHF)框架,用于管理LLM模型。最后,文章提供了一个实用的方法,以规模化地将生成式AI应用于生产,重点是利用现有的LLM,通过微调或提示工程来个性化它们,同时实施措施以降低风险,并保持设计灵活以适应变化。
"如何利用Databricks、MosaicML和MLRun将Generative AI应用于生产环境?" "如何在复杂用例中实现Generative AI的实时协作和自动化?" "如何通过数据预处理和模型优化,提高Generative AI应用的性能和可扩展性?"
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