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为 AI 做好准备:从实际部署中吸取的经验教训.pdf

上传人: 哆哆 编号:631003 2025-04-19 14页 1.77MB

1、Getting Ready for AI:Lessons Learned from Real-Life DeploymentSmitha MathewsSmitha MathewsSnr.Manager Data&Analytics StrategyDec 12th,2024Founded in 1965;Industry-Leading Financial Profile with Scale,Scope,and Diversity2024 Analog Devices,Inc.All Rights Reserved.230%Automotive11%Communications13%3Co

2、nsumer46%Industrial21.Information as of end of fiscal year 20242.Industrial automation,healthcare,aerospace,instrumentation and measurement,and energy management3.The sum of the individual percentages may not equal the total due to rounding.$110.7BMarket Cap as of End of Fiscal Q4 2024$20BRevenue Gr

3、owth Aspirations by 2030$9.4B12024 AnnualRevenueAnalog and Mixed-SignalPower ManagementRadio Frequency and MicrowaveEdge Processors and SensorsAI and Machine LearningTECHNOLOGIES75k75k2Products125k+125k+2customers acrossdiverse markets andgeographies80%80%2 of revenue derived from products that indi

4、vidually contribute 0.1%or lessWhat is Ai doing for you?2024 Analog Devices,Inc.All Rights Reserved.32024 Analog Devices,Inc.All Rights Reserved.4DATAHealthyThe ASK:Scale Exensio at ADIThe ASK:Scale Exensio at ADI SCOPE:Manufacturing Analytics for all productsTest Operations in 4 factories/6 OSATs T

5、HE CHALLENGE:Integrate 3 legacy company data standardsDeploy in 1 year with 2 years of backfill!2024 Analog Devices,Inc.All Rights Reserved.520232024 Analog Devices,Inc.All Rights Reserved.6150+users actively using Exensio to solve real-life problemsCentralized data from 3 legacy companiesTest Opera

6、tions in 4 factories,1000+testers&common reports and controls4 OSATs data integration imminentAMBITION2024 Analog Devices,Inc.All Rights Reserved.720241000+users actively using Exensio for problem solvingSignificant reduction in analysis cycle times,Successful handling of large data setsData integra

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本文主要介绍了 Analog.com 公司如何为人工智能(AI)做准备,并从实际部署中吸取的教训。文章指出,成立于1965年的 Analog.com 是一家具有行业领先地位、规模、范围和多样性的金融公司,其业务涵盖汽车、通信、消费和工业等领域。公司目前的市场资本化为 1107 亿美元,收入增长目标为 2030 年的 200 亿美元。文章强调了数据在 AI 中的重要性,并提出了在现有设施中实施 AI 的挑战,如整合多个数据标准和在一年内部署并回填两年数据。通过实施名为 Exensio 的中央化数据分析工具,公司已经成功地为 150 多名用户提供了解决实际问题的方案,并计划在 2024 年将用户数量增加到 1000 多名。此外,文章还讨论了数据质量的问题,并提出了改进数据质量的策略,如在数据源头实施清洁措施、统一标准、加强数据治理等。通过实施这些策略,公司的运营纪律得到了改善,数据可用性提高了 20%,历史数据质量提高了 30%,实时数据质量提高了 40%。
"AI在实际部署中的挑战与收获是什么?" "如何平衡大数据的规模与质量?" "如何在源头确保数据清洁并提高运营纪律?"
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