10 Questions
What is the primary benefit of the Anomaly Detector's multi-variate capability?
To detect dependencies and inter-correlations between different signals
What is a major advantage of the Anomaly Detector's AI models?
They are trained and customized for your data
What is an example of a system that can be protected by the Anomaly Detector's multivariate capability?
All of the above
What is the benefit of the Anomaly Detector's new multivariate anomaly detection APIs?
They enable developers to easily integrate advanced AI into their applications
What is an example of a signal that could be monitored by the Anomaly Detector?
Vibration
What is the primary advantage of using Multivariate Anomaly Detector (MVAD) according to Siemens Healthineers?
Short time to market due to the ready-to-use model
What is the primary benefit of using Anomaly Detector for Airbus?
Faster deployment of solutions due to the out-of-the-box solution
What is the primary challenge of using univariate time-series anomaly detection algorithms?
Problem in deciding whether the whole system is running normally
What is the primary application of time-series anomaly detection in the industry?
Predictive maintenance and quality control
What is the primary advantage of using Multivariate Anomaly Detector (MVAD) over traditional anomaly detection methods?
Ability to analyze multiple time-series metrics simultaneously
Study Notes
Anomaly Detector in AIOps and Predictive Maintenance
- Anomaly Detector, a Cognitive Service, is used to build metrics monitoring solutions for AIOps and predictive maintenance, leveraging its easy-to-use time-series anomaly detection.
- It analyzes time-series individually, providing simplicity and scalability.
New Multivariate Capability in Anomaly Detector
- The new multivariate anomaly detection APIs in Anomaly Detector enable developers to integrate advanced AI for detecting anomalies from groups of metrics into their applications without machine learning knowledge or labeled data.
- The feature considers dependencies and inter-correlations between different signals, protecting mission-critical systems and physical assets from failures with a holistic view.
Multivariate Anomaly Detection in Real-World Applications
- The feature can detect anomalies in complex systems, such as 20 sensors from an auto engine generating 20 different signals, and sense anomalies like a seasoned floor expert.
- The AI models are trained and customized for specific business data, understanding the business and integrating easily into predictive maintenance solutions, AIOps monitoring solutions, or business intelligence tools.
Customer Experience with Anomaly Detector
- Siemens Healthineers uses Multivariate Anomaly Detector (MVAD) in medical device stress tests during the final inspection in production, finding it easy to use and effective.
- Airbus deployed Anomaly Detector to gather and analyze telemetry data, foreseeing and fixing potential problems before they occur, saving up to three months on development for smaller use cases.
Importance of Time-Series Anomaly Detection
- Time-series anomaly detection is an important research topic in data mining with a wide range of applications in the industry.
- Efficient and accurate anomaly detection helps companies monitor key metrics continuously and alert for potential incidents on time.
Learn about Microsoft's Anomaly Detector, a cognitive service that enables easy time-series anomaly detection for AIOps and predictive maintenance. Discover its new multi-variate capability and benefits.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
