Broadcom announced the availability of the latest generation of AIOps from Broadcom, an open platform with artificial intelligence, machine learning and end-to-end observability that helps organizations achieve operational excellence.
AIOps allows business and IT leaders to manage critical KPIs that align IT outputs to business outcomes, driving digital agility, while proactively ensuring enhanced customer and positive employee experiences.
“Imagine a lens that provides a clear and fully integrated view of your business with IT providing valuable intelligence that drives informed decision-making. This is no longer a wish list, this is a reality for our customers through the new Broadcom AIOps solution,” said Serge Lucio, VP and GM, Enterprise Software Division, Broadcom. “AIOps from Broadcom provides enterprises with comprehensive observability across user experience, applications, infrastructure and networks delivering digital agility, actionable insights and intelligent automation— all enhancing business outcomes and customer experience.”
AIOps from Broadcom is an open platform that correlates and analyzes a broad range of IT observability data sources and acts as a trusted proof point for the IT Operations analytics offered in Broadcom's BizOps solution. AIOps from Broadcom now includes new AI/ML techniques and customizable views for enhanced actionable insights.
New capabilities include:
- Full-Stack Observability— for hybrid clouds, cloud-native applications, distributed tracing, mobile-to-mainframe insights and network telemetry while providing users openness to ingest other data sources quickly with 15 new third-party data integrations.
- DX Dashboards— provide rich visualizations for task-based persona-driven insights along with unfettered access to all data to easily customize and create company-specific dashboards.
- Service and Alarm Analytics— include additional machine learning topology and correlation, casual graphs and intelligent situational analysis for automatic root cause analysis, noise mitigation, alarm clustering, and anomaly detection.
- Intelligent Automation— based on a recommendation engine connects root cause analysis with extensive out-of-the-box actions and automation strategy based on problem scenarios.
- Capacity Analytics— provide service-aware configuration for capacity planning and drill-downs for service context and group context to identify the potential over-utilized or under-utilized resources and optimize the capacity allocation.
- Continuous Feedback Loops— connect data across development, IT and the business for views and insights across disparate data sets and siloed teams.
- Machine Learning— based on an extensive library of algorithms, natural language processing and an ontological graphical topology for rich situational correlation for continuous learning and improved automated root cause analysis.
- Software to Silicon Insights— apply AI and ML to rich, granular data capture at the chip level to enable a unique, AI-driven solution for real-time proactive network congestion and packet loss triage.
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