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LogicMonitor Enhances LM Intelligence AIOps Early Warning System

LogicMonitor announced enhancements to its LM Intelligence™ AIOps early warning system.

Its dynamic thresholds functionality, first introduced in December 2019, now includes support for seasonality and rate of change. LogicMonitor’s dynamic thresholds intelligently alert IT teams based on historical performance and newly refined algorithms to help businesses save time, avoid alert fatigue, and surface anomalies sooner to proactively prevent downtime.

With the introduction of seasonality support, LogicMonitor’s dynamic thresholds now detect patterns in performance and ensure alerts are triggered for anomalies outside of those patterns. The new rate of change algorithms, added as part of this July 2020 enhancement, detects anomalies in the rate at which a metric value is changing as compared to its normal pattern, to enable users to identify issues before they result in a negative impact to the business.

LogicMonitor’s dynamic thresholds use historical performance to generate an expected range for resources, and alert on anomalies that exceed this expected range. Dynamic thresholds significantly reduce alert noise by silencing notifications for static threshold-generated alerts corresponding to normal performance within the expected range.

“If static thresholds are not set or tuned well, dynamic thresholds will ensure alerts are triggered and silenced appropriately. If static thresholds are tuned well and enabled, dynamic thresholds will still defer to them,” said Tej Redkar, CPO at LogicMonitor. “The addition of dynamic thresholds seasonality and rate of change support to the existing anomaly detection, forecasting and root cause analysis features of LM Intelligence mean that LogicMonitor customers now have access to the most advanced AIOps capabilities in the market.”

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LogicMonitor Enhances LM Intelligence AIOps Early Warning System

LogicMonitor announced enhancements to its LM Intelligence™ AIOps early warning system.

Its dynamic thresholds functionality, first introduced in December 2019, now includes support for seasonality and rate of change. LogicMonitor’s dynamic thresholds intelligently alert IT teams based on historical performance and newly refined algorithms to help businesses save time, avoid alert fatigue, and surface anomalies sooner to proactively prevent downtime.

With the introduction of seasonality support, LogicMonitor’s dynamic thresholds now detect patterns in performance and ensure alerts are triggered for anomalies outside of those patterns. The new rate of change algorithms, added as part of this July 2020 enhancement, detects anomalies in the rate at which a metric value is changing as compared to its normal pattern, to enable users to identify issues before they result in a negative impact to the business.

LogicMonitor’s dynamic thresholds use historical performance to generate an expected range for resources, and alert on anomalies that exceed this expected range. Dynamic thresholds significantly reduce alert noise by silencing notifications for static threshold-generated alerts corresponding to normal performance within the expected range.

“If static thresholds are not set or tuned well, dynamic thresholds will ensure alerts are triggered and silenced appropriately. If static thresholds are tuned well and enabled, dynamic thresholds will still defer to them,” said Tej Redkar, CPO at LogicMonitor. “The addition of dynamic thresholds seasonality and rate of change support to the existing anomaly detection, forecasting and root cause analysis features of LM Intelligence mean that LogicMonitor customers now have access to the most advanced AIOps capabilities in the market.”

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Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...