談金融服務領域的機器學習最佳實踐

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Original URL: "},{"type":"link","attrs":{"href":"https:\/\/aws.amazon.com\/cn\/blogs\/machine-learning\/machine-learning-best-practices-in-financial-services\/","title":"","type":null},"content":[{"type":"text","text":"https:\/\/aws.amazon.com\/cn\/blogs\/machine-learning\/machine-learning-best-practices-in-financial-services\/"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"金融服務領域的機器學習最佳實踐"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在最近發佈的白皮書《金融服務中的機器學習最佳實踐（"},{"type":"link","attrs":{"href":"https:\/\/d1.awsstatic-china.com\/whitepapers\/machine-learning-in-financial-services-on-aws.pdf","title":"","type":null},"content":[{"type":"text","text":"Machine Learning Best Practices in Financial Services"}]},{"type":"text","text":"）》中，我們概述了在構建機器學習（ML）工作流的過程中，金融機構需要關注的安全性與模型治理注意事項。這份白皮書還涵蓋了常見的安全性與合規性要素，旨在配合上手"},{"type":"link","attrs":{"href":"https:\/\/www.youtube.com\/watch?v=HlSEUvApDZE","title":"","type":null},"content":[{"type":"text","text":"演示"}]},{"type":"text","text":"與"},{"type":"link","attrs":{"href":"https:\/\/sagemaker-workshop.com\/securityforsysops.html","title":"","type":null},"content":[{"type":"text","text":"研習班"}]},{"type":"text","text":"共同爲您介紹端到端的示例。雖然這份白皮書主要着眼於金融服務行業，但其中涉及的身份驗證與訪問管理、數據與模型安全以及ML實施（MLOps）最佳實踐等內容，也同樣適用於醫療保健等其他受到嚴格監管的行業。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如下圖所示，典型的機器學習工作流中往往涉及多個利益相關方。爲了成功管理並運營這類工作流，我們需要推動跨團隊協作，將業務相關方、系統運營管理員、數據工程師以及軟件\/DevOps工程師納入這套體系中來。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/33\/20\/3317edc40a2c6d11dcfb5301fab98f20.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":false}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在白皮書中，我們還探討了各個團隊需要關注的核心事項，並通過示例與說明闡述如何使用"},{"type":"link","attrs":{"href":"https:\/\/amazonaws-china.com\/sagemaker","title":"","type":null},"content":[{"type":"text","text":"Amazon SageMaker"}]},{"type":"text","text":"及其他AWS服務實現機器學習工作負載的構建、訓練與部署。具體來講，結合高監管要求背景下客戶們提供的真實反饋，我們着重分析了以下主題："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"置備一套安全的機器學習環境——具體包括："}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"\t* 計算與網絡隔離——如何在不連接互聯網的前提下，將Amazon SageMaker部署在客戶的專用網絡當中。"}]}]}