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Netscout Integrates High-quality AI-ready Data to Train AI Models

NETSCOUT SYSTEMS, INC. announced a new initiative designed to deliver the valued data needed to secure better outcomes from artificial intelligence (AI) and AIOps solutions. As enterprises increase observability for AIOps, they require high-fidelity, enriched, and curated data mapped to their unique requirements. NETSCOUT is working across its growing partner ecosystem, including Cisco, Palo Alto Networks, ServiceNow, Splunk, and others, to integrate its high-quality AI-ready data and deliver the insights needed to drive better business outcomes and elevate user effectiveness. 

NETSCOUT generates granular telemetry at scale and curates it into targeted feeds, producing an information-rich dataset designed to address critical use cases. NETSCOUT’s data can be easily ingested into data lakes and other AIOps platforms, where it can be enriched and combined with other datasets for analytics and visualization, resulting in higher-quality behavioral classifications, predictions, and automation. 

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Delivering Data Insights at AI Scale 

To provide high-quality AI-ready data that mitigates the risks IT teams face from an overload of unusable or ineffective data, NETSCOUT has introduced Omnis™ AI Insights. It delivers precise, actionable network telemetry data that feeds customer AI initiatives to enable critical outcomes without requiring data transformations and adaptations, including:  

  • identifying and correlating observability trends  
  • streamlining and automating data analysis 
  • uncovering historical operational patterns  
  • detecting unforeseen issues and security risks that could lead to future service outages and data breaches 

Data intelligence, produced and curated at the source for AI platforms, is imperative for AI use cases across multiple sectors, including healthcare, communication service providers, financial services, insurance, manufacturing, energy and utilities. NETSCOUT’s high-quality AI-ready data allows models to converge and generalize without dealing with extraneous data that blurs or impairs outcomes. 

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