Target 子公司 Consensus 如何简化机器学习的数据整理
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Trifacta Wrangler Pro
- DataRobot
技术栈
- Amazon S3
- Amazon Redshift
- Amazon EMR
- AWS VPC
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 数据即服务
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 零售
适用功能
- 商业运营
- 销售与市场营销
用例
- 欺诈识别
服务
- 数据科学服务
- 系统集成
关于客户
Consensus Corporation 是 Target 的子公司，致力于简化销售联网设备的复杂流程。该公司使用单一平台将零售商与制造商和网络运营商连接起来，实现所谓的多元化销售：将技术和服务购买捆绑在一起，例如智能手机和数据计划，或智能电视和视频流服务订阅。Consensus 使全国各地的零售店能够通过一个统一的在线平台销售这些类型的联网设备和服务。然而，销售昂贵设备和服务的零售商面临的一个主要风险是欺诈性客户活动。为了应对这一风险，Consensus 将欺诈预防作为其核心服务之一。
挑战
Consensus Corporation 是 Target 的子公司，它简化了销售联网设备的复杂流程。然而，对于销售昂贵设备和服务的零售商来说，一个主要的风险是欺诈性客户活动。为了应对这一风险，Consensus 将欺诈预防作为其核心服务之一。通过其自动化机器学习驱动的在线引擎，Consensus 可以在其零售商客户购买昂贵设备之前提醒他们注意高风险消费者。为了识别潜在的欺诈行为，Consensus 建立了一个先进的数据模型，该模型利用大量不同的数据并进行定期更新。为了能够不断完善其预测模型并更快地提醒其零售商客户注意潜在的欺诈行为，Consensus 寻找能够更快地准备这些数据以用于其机器学习模型的技术。重新设计 SQL 脚本的艰苦过程使 Consensus 平均需要长达六周的时间来更新其欺诈检测机器学习模型。此外，数据准备过程需要复杂的数据科学技术知识，这使得公司的产品和商业智能团队无法独自执行数据准备任务。
解决方案
Consensus 发现，需要更好的数据准备和快速的模型原型设计和评估，而这些不需要高级数据科学知识。起初，该公司尝试使用几种第三方数据准备解决方案，但发现它们要么昂贵、笨重，要么部署复杂。最后，Consensus 产品开发高级总监 Harrison Lynch 发现了 DataRobot 自动化机器学习平台和免费的 Trifacta Wrangler 解决方案。Trifacta 的用户友好型 UI 和 DataRobot 快速构建和部署机器学习模型的能力有助于使这两种技术在竞争中脱颖而出。Consensus 随后在 AWS 上选择了 DataRobot 和 Trifacta Wrangler Pro，以获得更强大的功能和更多的连接性，以处理更多数据并快速创建机器学习模型。Trifacta Wrangler Pro 和 DataRobot 是 AWS 部署的解决方案，可以无缝访问存储在 AWS 上的数据，包括 Amazon S3 和 Amazon Redshift。此外，Trifacta Wrangler Pro 利用 Amazon EMR (Elastic MapReduce) 来处理数据。
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