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features
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support

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Description

Anticipate issues before they arise by utilizing an Azure AI anomaly detection service. This service allows for the seamless integration of time-series anomaly detection features into applications, enabling users to quickly pinpoint problems. The AI Anomaly Detector processes various types of time-series data and intelligently chooses the most effective anomaly detection algorithm tailored to your specific dataset, ensuring superior accuracy. It can identify sudden spikes, drops, deviations from established patterns, and changes in trends using both univariate and multivariate APIs. Users can personalize the service to recognize different levels of anomalies based on their needs. The anomaly detection service can be deployed flexibly, whether in the cloud or at the intelligent edge. With a robust inference engine, the service evaluates your time-series dataset and automatically determines the ideal detection algorithm, enhancing accuracy for your unique context. This automatic detection process removes the necessity for labeled training data, enabling you to save valuable time and concentrate on addressing issues promptly as they arise. By leveraging advanced technology, organizations can enhance their operational efficiency and maintain a proactive approach to problem-solving.

Description

Safeguard business service-level agreements by utilizing dashboards that enable monitoring of service health, troubleshooting alerts, and conducting root cause analyses. Enhance mean time to resolution (MTTR) through real-time event correlation, automated incident prioritization, and seamless integrations with IT service management (ITSM) and orchestration tools. Leverage advanced analytics, including anomaly detection, adaptive thresholding, and predictive health scoring, to keep an eye on key performance indicators (KPIs) and proactively avert potential issues up to 30 minutes ahead of time. Track performance in alignment with business operations through ready-made dashboards that not only display service health but also visually link services to their underlying infrastructure. Employ side-by-side comparisons of various services while correlating metrics over time to uncover root causes effectively. Utilize machine learning algorithms alongside historical service health scores to forecast future incidents accurately. Implement adaptive thresholding and anomaly detection techniques that automatically refine rules based on previously observed behaviors, ensuring that your alerts remain relevant and timely. This continuous monitoring and adjustment of thresholds can significantly enhance operational efficiency.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AXYVOR No 
Activate IAM Yes 
Azure AI Metrics Advisor Yes 
Bing Yes 
CTM360 Yes 
CloudApper iPaaS No 
CloudFabrix No 
Compliance Warden Yes 
Crestwood Cloud Yes 
Evolven No 
Microsoft Azure Yes 
Microsoft Office 2021 Yes 
Qualio Yes 
Rayven Yes 
SAP Store No 
Splunk User Behavior Analytics No 
Sprinklr Yes 
The Galileo Suite No 
groundcover Yes 

Integrations

AXYVOR Yes 
Activate IAM No 
Azure AI Metrics Advisor No 
Bing No 
CTM360 No 
CloudApper iPaaS Yes 
CloudFabrix Yes 
Compliance Warden No 
Crestwood Cloud No 
Evolven Yes 
Microsoft Azure No 
Microsoft Office 2021 No 
Qualio No 
Rayven No 
SAP Store Yes 
Splunk User Behavior Analytics Yes 
Sprinklr No 
The Galileo Suite Yes 
groundcover No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

azure.microsoft.com/en-us/products/ai-services/ai-anomaly-detector/

Vendor Details

Company Name

Cisco

Founded

1984

Country

United States

Website

www.splunk.com/en_us/products/it-service-intelligence.html

Product Features

IT Alerting

Alert Noise Reduction No 
Alert Routing No 
Dynamic Notifications No 
Enriched Incident Context No 
Escalation Policies No 
Incident History Audit No 
Multi-User Alerting No 
Multiple Alert Types No 
On-Call Management No 
Rich HTML Email Notifications No 

IT Service

Contract Management No 
IT Asset Management No 
Incident Management No 
Knowledge Management No 
Release Management No 
Self Service Portal No 
Service Catalog No 
Service Reporting No 
Ticket Management No 

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