Streamable Multivariate Time Series Anomaly Detection Specialist
vor 3 Wochen
Experience:
Bachelor- / Masterthesis
Accelerating business to improve the lives of people. This is our purpose statement and encapsulates what we enthusiastically do every day.
We integrate our customers' IT systems to make sure that the right data is at the right place at the right time when they digitalize their processes.
Our success story began many years ago, when we helped the German automotive industry to digitalize their paper-based supply chains.
Todays, SEEBURGER is a leading global B2B software provider with more than 1, #businessaccelerators in 15 countries worldwide and over 10, satisfied customers that rely on our innovative solutions.
Topic:
Streamable Multivariate Time Series Anomaly Detection for Cloud Service Infrastructures
Motivation and Goals:
Automatic anomaly detection is an important tool for monitoring complex cloud service infrastructures for B2B communications. Multivariate anomalies here arise simultaneously from a variety of metrics and the context of individual services.
In operation, previously unknown or rare errors occur, comparatively few anomalies can be labeled by experts, and data for training ML models are insufficiently cleaned of anomalies.
Tasks:
- Investigation and evaluation of different approaches for anomaly detection with a focus on Deep Neural Networks.
- Pre-processing, filtering, cleaning, as well as enrichment of monitoring data, message tracking data, and the cloud structure data for the anomaly detector.
- Development and implementation of the AI anomaly detector as well as a framework for the regular training of the ML models and the stream-oriented detection of anomalies.
- Development and implementation of a dynamic alert system suitable for different users such as system operators or customers, as well as analysis and evaluation of the anomalies.
- Development of criteria for the evaluation of the system.
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