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Anomaly Detection in Real-Time Data Streams

The Cortana Intelligence IT Anomaly Insights solution helps IT departments within large organisations to quickly detect and fix issues based on underlying health metrics from IT infrastructure (CPU, Memory, etc.), services (Timeouts, SLA variations, Brownouts, etc.), and other key performance indicators (KPIs) (Order backlog, Login and Payment failures, etc.) in an automated and scalable manner. This solution also offers an easy “Try it Now” experience that can be tried with customised data to realise the value offered by the solution. The “Deploy” experience allows you to quickly get started with the solution on Azure by deploying the end-to-end solution components into your Azure subscription and providing full control for customisation as needed.

实时数据流异常检测Cortana Intelligence IT 异常见解解决方案帮助大型组织中的 IT 部门以可缩放的自动化方式,根据 IT 基础结构(CPU,内存等)、服务(超时,SLA 变体,限电等)的基础运行状况指标和其他关键性能指标 (KPI)(订单积压、登录和付款失败等)快速检测并修复问题。此解决方案还提供简单的“立即试用”体验,可使用自定义数据进行尝试,以实现解决方案提供的价值。通过“部署”体验,可将端到端解决方案组件部署到 Azure 订阅中,并根据需要对自定义进行完全控制,从而在 Azure 上快速开始使用该解决方案。

Disclaimer

©2017 Microsoft Corporation. All rights reserved. This information is provided “as is” and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third-party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets.

实时数据流异常检测Cortana Intelligence IT 异常见解解决方案帮助大型组织中的 IT 部门以可缩放的自动化方式,根据 IT 基础结构(CPU,内存等)、服务(超时,SLA 变体,限电等)的基础运行状况指标和其他关键性能指标 (KPI)(订单积压、登录和付款失败等)快速检测并修复问题。此解决方案还提供简单的“立即试用”体验,可使用自定义数据进行尝试,以实现解决方案提供的价值。通过“部署”体验,可将端到端解决方案组件部署到 Azure 订阅中,并根据需要对自定义进行完全控制,从而在 Azure 上快速开始使用该解决方案。

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Quality assurance systems allow businesses to prevent defects throughout their processes of delivering goods or services to customers. Building such a system that collects data and identifies potential problems along a pipeline can provide enormous advantages. For example, in digital manufacturing, quality assurance across the assembly line is imperative. Identifying slowdowns and potential failures before they occur rather than after they are detected can help companies reduce the cost of scrap and rework, while improving productivity.