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Machine learningKeysight Technologies has introduced new features for Machine learning in the active Network monitoring platform Hawkeye by Ixia announced. By adding machine learning, Keysight helps companies reduce downtime and improve network uptime. It can quickly predict, identify and correct network anomalies.

The amount and speed of raw data from networks and applications continues to increase. The network operators are faced with a flood of warnings. These network teams need to avoid alarm fatigue while improving their ability to troubleshoot network and application problems.

In response, machine learning, including machine learning, has proven to be a good method for gaining knowledge from huge amounts of data. By 2022, according to Gartner, over 50% of the newly developed applications will be implemented based on machine learning or artificial intelligence.

Machine learning supports network teams

"Keysights Hawkeye uses machine learning to help network operations teams understand their increasingly complex networks," said Recep Ozdag, vice president and general manager of visibility at Keysights Network Applications & Security Group. “Network operations teams struggle to correlate basic performance metrics with actual network problems. The new machine learning functions offer an insight into useful variants. This enables the teams to be quickly alerted to real overloads, problems and application performance failures. ”

Hawkeye has automatic threshold and outlier detection. This combines machine learning-based problem detection with customizable sensitivity criteria. It gets through the mess and immediately notifies the network teams of problems. An outlier dashboard enables these teams to identify potential problems at a glance. It also offers integrated, detailed visualizations that help analyze and solve the root cause.


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