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Use case

Enabling smarter anomaly detection with ML-driven observability - Ativa™

Modern networks demand more than static thresholds to detect anomalies. With dynamic environments and increasing complexity, traditional methods fall short, causing delayed issue detection, false positives, and operational inefficiencies.

With Ativa’s AI/ML-powered solution, ensure accurate anomaly detection, root-cause analysis, and proactive network monitoring, while eliminating manual thresholds with its intelligent approach, reducing alarm noise, and enhancing operational efficiency.

In this use case, learn how to:

  • Detect and resolve network issues proactively with machine learning.
  • Minimize false positives and streamline troubleshooting.
  • Classify root causes of multiple degradations in real-time.
  • Improve scalability and operational efficiency with automated processes.

Download the use case now to see how to transform anomaly detection and deliver consistent, top-tier network performance. 

Preview image of Ativa use case: Machine learning for smart observability

Discover how to optimize detect anomalies, reduce false alerts, and ensure top network performance