Written by John Stimmel, Principal Cloud Specialist Account Executive, LogicMonitor It’s common knowledge that Kubernetes (commonly referred to as “K8”s) container management and orchestration provide business value by enabling cloud-native agility and superior customer experiences. By their nature, the speed and agility of Kubernetes platforms come with complexity.

In today’s fast-paced IT ecosystem, agility and efficiency are not just goals but necessities. So why waste an hour (or more) manually onboarding individual devices when you can leverage the LogicMonitor API to automate the onboarding process for an entire site in just minutes from a simple CSV file? In this article, we’re going to review how LogicMonitor administrators can maximize efficiency and transform their IT operations using LogicMonitor’s REST API and Powershell.

LogicMonitor’s agentless collection has long provided customers with many benefits for collecting telemetry data directly from network devices. Recently, LogicMonitor added another feature, enabling the discovery of devices/sites and the collection of telemetry data from the Cisco Catalyst Center. Retaining options is essential due to the pros and cons associated with each approach.

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.

Rules are excellent when you know precisely what you want to match, typically based on experience. Yet rules only let you observe what you have learned to look for. This is where artificial intelligence (AI) and machine learning (ML) contribute significantly to observability – detecting errors and early warning signs that were previously unobservable. LM Envision supports metric and log anomaly detection. This blog discusses how LM Envision Log Anomalies uncovers previously unknown anomalies.

IT operations are a constant juggling act. Too many tools, too much data, and not enough time to proactively avoid outages. But what if AI could not only streamline your tools but help you make sense of the chaos? That’s the power of AIOps.

At LogicMonitor, we are deeply committed to a mission that goes beyond the conventional: revolutionizing IT monitoring through hybrid observability powered by AI. This ambition is not merely a slogan but the cornerstone of our entire approach. Our LM Envision platform was purposely designed to bring together diverse IT environments under one seamless, integrated experience. Enterprises have complex IT ecosystems.

IT monitoring is evolving rapidly, and LogicMonitor is at the forefront of this transformation with the release of LogicMonitor’s new user interface (UI). This release marks a significant milestone, reflecting our commitment to innovation, responsiveness to user feedback, and anticipation of future technological trends.

LogicMonitor has long been synonymous with observation — a platform that keenly watches over IT environments, alerting teams to potential issues. However, the age-old challenge remained: how to seamlessly transition from observation to action. Enter the LogicMonitor event-driven ansible integration with RedHat. What sets this solution apart is the fact our teams worked together to build it.

LogicMonitor spent the week in Las Vegas at AWS re:Invent 2023. With over 50,000 attendees, it was packed with groundbreaking keynote sessions, networking events, and so much more. One major announcement during the event was AWS Marketplace’s Quick Launch. Made to easily deploy SaaS products, interested users can easily buy and deploy LogicMonitor straight from the AWS Marketplace.