Augtera Networks, the industry leader in AI/ML-powered Network Operations Solutionsannounced the first Network AIOps solution for Network logs, ushering in a new era of real-time, automated, log experiences.
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“The semantic understanding of log messages in real-time is much more powerful than simple text searches and matching”
“Earlier this year we announced our industry-first real-time Zero Day Anomalies technology for Syslog based on purpose-built, high-performance, Natural Language Processing (NLP),” said Rahul Aggarwal, Founder and CEO of Augtera Networks. we are pleased to announce our expanded LogAI solution for all types and formats of logs that can be ingested in the Augtera platform as JSON over Kafka or using our APIs. No other log solution provides this real-time NLP based capability, and LogAI does it at high scale, with high-performance, and high-efficiency.”
Previously, Augtera Networks had announced its industry-leading real-time NLP-capability for Syslog. Today, Augtera Networks announces the full LogAI solution, for any log format, which includes:
- Support for an expanding number of log formats/transports including Syslog, JSON, JSON over Kafka, and an Augtera API.
- Zero Day Anomaly detection of “unknown unknown” new and rare syslog messages that often precede outages.
- Collective Learning across the entire Augtera Networks customer base of high-fidelity log classifiers.
- Purpose-built rate change ML algorithms that identify log message bursts with high fidelity and low false positives
- Extraction of metrics embedded in log messages with the ability to apply metric algorithms for anomaly detection and other Network AI capabilities.
- Tag-based structured log search
- Noiseless integration into ServiceNow, Slack, Automation, and other upstream systems.
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Logs are a rich source of network telemetry information for network operations. While there are several tools that exist for historical analysis and querying of logs, they have not provided real-time anomaly detection capabilities that leverage the richness of text-based logs. This is increasingly relevant as operators want to find needles automatically and proactively in the growing haystack of log data. In addition, the volume, velocity, and variety of log messages calls for new approaches that generate structure and efficient analysis from unstructured text messages.
“The semantic understanding of log messages in real-time is much more powerful than simple text searches and matching,” said Bhupesh Kothari, Co-Founder and VP of Engineering. “We have expanded our platform to apply our Network AI technology including our purpose-built real-time Natural Language Processing innovation across multiple Network Operations logs.”
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