在这个议题里， 我们会概述这个领域内的用户痛点，我们会介绍Kensho, 一个基于AI的商业指标监控与诊断工具, 我们通过将AI元素注入这个BI工具，从而构建来服务不同的微软团队的历程。我们的从中学到的经验教训，技术的选择和演化，架构，算法等等。通过工程+数据科学解决了一个工业界的一个通用需求。另外我们会展望基于Kensho架构的下一代智能BI的解决方案。
At Microsoft, there is a common need to monitor product and business metrics to stay on top of product and business health and act quickly to address issues deviating from the historical pattern. The need is simple and clear. However, it turns out to be extremely difficult to have a scalable system that is also smart enough to provide automated anomaly detection across a wide spectrum of time series related to business or product performance as well as automated diagnostic insight into why that business incident happens.
Tony Xing and Bixiong Xu explore pain points from Microsoft customers and detail Microsoft’s path to implementing Kensho, an automated business incident monitoring and diagnostics system powered by AI to serve different Microsoft teams. Tony and Bixiong cover lessons learned during the process, the evolution of the technology, the solution architecture, and the algorithms and explain how collectively this combination of engineering and algorithms solve a common need that is applicable not only to Microsoft but to the industry as a whole.
Tony Xing is a senior product manager in AIDI (the AI, data, and infrastructure team) within Microsoft’s AI and Research organization. Previously, he was a senior product manager on the Skype data team within Microsoft’s Application and Service Group, where he worked on products for data ingestion, real-time data analytics, and the data quality platform.
Bixiong Xu is the principal dev manager on the AI, data, and infrastructure team at Microsoft.
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