The manufacturing sector is at a crossroads. Industry 4.0 brought with it a wave of innovation, with the industrial internet of things (IIoT), advanced automated, and AI-driven analytics. Now, we’re experiencing the onset of Industry 5.0, where humans work alongside smart machines to create more sustainable products, services, and supply chains.

LogicMonitor stands at the forefront of innovation in IT infrastructure monitoring, and our newest solutions help our customers optimize performance, manage costs, and gain deeper visibility into their network operations. Our vision is to empower businesses with the observability needed to navigate modern IT complexities with AI-powered solutions that drive efficiency.

As the healthcare industry has introduced more complex IT infrastructure, it now faces many challenges as it strives to deliver high-quality services to patients. From adapting to remote work and telemedicine to resource constraints, healthcare organizations must continually adapt to new technologies. Some of the nascent technologies, like remote triage of patients, telemedicine, and IoT, have all seen an acceleration in innovation as the industry pivots to visit patients remotely.

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.