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Anomaly Detection

Improved anomaly detection and faster root cause analysis: the latest features in Grafana Cloud Application Observability

In recent years, “the biggest needs we’ve heard from our customers have been to make it easier to understand their observability data, to extend observability into the application layer, and to get deeper, contextualized analytics,” said Tom Wilkie, CTO of Grafana Labs, at ObservabilityCON 2023.

Ways to Detect Anomalies in Azure with Real-Life Examples

Azure MVP, Michael Stephenson (aka Mike) walks through a practical example of Azure anomaly detection and the importance of keeping an eye on costs in Azure. Using Turbo360's cost analyzer module, Mike shows us how to efficiently manage everyday expenses and save money. Mike shares a scenario where he manages a Data Gateway service on Azure within a limited budget. He then explains how changes in resource scheduling led to unexpected cost increases. Using the anomaly detection feature, he identifies the abnormal cost behavior and discusses the significance of promptly addressing such issues.

What is an Anomaly? Avoiding False Positives in Watchdog Detected Anomalies

In 2018 Datadog released Watchdog to proactively detect anomalies on your observability data. But what defines an anomaly? How do you avoid false positives? At Datadog Summit London 2024, Nils Bunge, product manager at Datadog, shared the story of the creation of the first Datadog AI feature (Watchdog Alert), what we learned from it and how we applied those lessons to all the added AI functionalities across the years.

Azure Cost Monitoring: Anomaly Detection

In this video, Michael Stephenson demonstrates the anomaly detection feature in Turbo360, focusing on enhancements in cost monitoring. This feature allows users to easily identify deviations in Azure costs and receive alerts when anomalies occur. It simplifies configuration, enabling daily monitoring with alerts sent to specified channels. Anomalies can be detected at both high-level and granular levels, aiding in overall Azure cost management.

How to enhance network monitoring: 3 anomaly detection use cases

In the LM Envision platform, anomaly detection for metrics is referred to by the feature name “Dynamic Threshold” rather than the more generic machine learning term “anomaly detection.” Dynamic thresholds allow users to identify and set custom alert thresholds based on observed data points. Metric thresholds in rules-based systems are effective when the desired outcome is clear. However, static thresholds may not anticipate emerging issues.

VictoriaMetrics Machine Learning takes monitoring to the next level

Today we’re happy to announce our new VictoriaMetrics Anomaly Detection solution, which harnesses machine learning to make database alerts more relevant, accurate and actionable for enterprise customers. VictoriaMetrics Anomaly Detection lightens the load on overworked data engineers, focusing their scarce resources on the alerts that matter most to their organization.

How Azure cost anomaly detection shields billing shocks

One of the fundamental promises of the cloud, when organizations embrace it, is significant cost savings compared to its on-premises costs. However, organizations to realize savings is required to proactively plan and monitor the application’s cost at a granular level. Azure cost anomaly detection involves promptly identifying, rectifying, and analysing unexpected Azure cost events to minimize their impact on the business.

Critical Automation: Anomaly Detection for Application Observability

There’s no debate — in our increasingly AI-driven, lean and data-heavy world, automating key tasks to increase effectiveness and efficiency is the ultimate name of the game. No matter what job you hold today, you’re likely being pushed to not only do more with less, but also perform your work with a tighter focus on specific outcomes and SLOs.

How AIOps turns anomaly detection into faster incident resolution

Quickly finding and resolving monitoring anomalies can make all the difference between service issues – and service excellence. But it’s far from easy, whether you’re trying to sift through countless alerts, understand the context behind anomalies, or swiftly pinpoint their root causes. If you’re an ITOps practitioner or enterprise architect looking to fine-tune your anomaly detection and resolution skills, you’ve come to the right place.
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Revealing Suspicious VPN Activity with Anomaly Detection

Anybody who monitors logs of any kinds, knows that the extracting useful information from the gigabytes of data being collected remains one of the biggest challenges. One of the more important metrics to keep an eye on are all sorts of logons that occur in your network – especially if they originate on the Internet – such as VPN logins.