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基于AWS的数据科学实践
book

基于AWS的数据科学实践

by Chris Fregly, Antje Barth
September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
China Electric Power Press Ltd.
Content preview from 基于AWS的数据科学实践
数据科学的应用场景
|
53
2. Forecast
从
S3
接入需求数据并创建新预测。
3. Lambda
函数将新的预测复制到
Amazon DynamoDB
。
4. Amazon EC2
集群容量扩缩器从
DynamoDB
读取预测并根据预测的需求调整
warm-pool
缓存的大小。
2.5
使用
Amazon Fraud Detector
识别虚假账号
每年,全世界的在线欺诈导致数百亿美元的损失。线上经营的公司尤其易于受到恶
意攻击,这些攻击尝试创建虚假用户账号并使用被盗信用卡购买商品来欺骗系统。
典型的欺诈检测系统通常依赖于商业规则来检测恶意攻击,这些规则难以快速适应
最新的欺诈手段。
有效的欺诈检测和防隐私泄漏系统包括如下的特征:
在影响业务之前阻断恶意攻击
在恶意攻击实际产生伤害之前标记可疑活动。
无需过多数据的高质量欺诈检测模型
预训练的算法可以分析最小数量的历史事件数据且仍能提供高质量的欺诈检测
模型。
使反欺诈团队更快行动并拥有更多控制权
在新事件数据可用时,自动处理构建、训练、微调、部署和更新欺诈检测模型
的复杂任务。
Amazon Fraud Detector
(欺诈检测器)是一个完全托管的服务,用于识别潜在的在
线欺诈活动,比如在线支付和虚假账号。
Amazon Fraud Detector
使用机器学习,其
基于
AWS
和
Amazon.com
在
20
年以来进行欺诈检测的经验。
通过
Amazon Fraud Detector
,我们通过数次点击、相对较小数量的历史数据及最少
的代码就可以创建一个欺诈检测模型。我们只需要上传诸如在线交易和账号注册的 ...
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Publisher Resources

ISBN: 9787519869663