Operations | Monitoring | ITSM | DevOps | Cloud

Actionable Network Device Monitoring with Automated Anomaly Detection and AI Troubleshooting

Network device monitoring is often a mess of polling, graphs, and alerts that don't lead to answers. In this webinar, we'll show how to monitor routers, switches, and firewalls in a way that quickly surfaces what matters: interface health, errors, drops, saturation, latency signals, and performance regressions—without drowning in noise. You'll learn how Netdata turns raw SNMP metrics into high-signal insights using automated anomaly detection and AI-assisted troubleshooting, so your team can move from 'something is wrong' to 'here's the root cause' faster.

AI SRE in Practice: Resolving Node Termination Events at Scale

When a node terminates unexpectedly in a Kubernetes cluster, the immediate symptoms are obvious. Workloads restart elsewhere, services experience partial outages, and alerts fire across multiple systems. The harder question is why it happened and how to prevent it from recurring. This scenario walks through a node termination event where the entire node pool was affected, requiring investigation across infrastructure layers to identify root cause and implement lasting remediation.

GenAI Observability in Grafana Cloud: End-to-End Agent Debugging (Demo)

From Observability for GenAI Applications (Grafana OpenTelemetry Community Call) We drill into traces to see which agents called which tools, where errors occurred, how long each LLM call took, and how costs and tokens are distributed. The walkthrough also covers using AI assistance to summarize long traces and identify optimization opportunities in real time..

AI Hosting: The Colocation vs. Cloud Dilemma for Your Next Project

Organisations running AI workloads, like banks training fraud detection models, hospitals testing diagnostic tools, or manufacturers using predictive analytics, all face the same problem: hosting them is costly and resource-intensive. They require dedicated GPUs running non-stop, vast amounts of data moving in and out, and far more power and cooling than a typical IT system.

AI Is Bigger Than LLMs: Why Network Teams Need to Think Beyond Chatbots and Agents

AI in network operations is more than chatbots and agents. LLMs make AI easier to use, but the real value comes from the underlying system of telemetry, data pipelines, analytics, ML models, domain knowledge, and workflows that help engineers reason, predict, and act. When designed thoughtfully, AI doesn’t replace engineers. Instead, it augments their expertise and reduces cognitive load across complex network operations.

From Trough to Traction: 10 Real-World Lessons in Cloud and AI Efficiency

When CloudZero CTO Erik Peterson joined the SourceForge podcast in January 2026, he didn’t just talk about cloud costs. He reframed them as a launchpad for innovation, survival, and competitive advantage. Whether he was describing the “trough of lost innovation,” the “freemium tax,” or why efficiency is the next frontier of engineering culture, Erik’s expert insights go beyond FinOps hygiene.

Agentic AI Essentials: Adoption Pitfalls and How to Avoid Them

In the last article in this series, we explored how IT professionals and leaders can cut through the hype surrounding agentic AI and gain a deeper understanding of what the technology actually offers. Now, we turn to the practical side: how to integrate it effectively. Let’s explore the challenges and outline strategies that organizations of all sizes can use to adopt agentic AI with confidence.

AI in Production Is Growing Faster Than We Can Trust it

Enterprise software has moved past the generative AI testing phase. Businesses with millions of daily users or workloads are no longer just prototyping LLMs in a vacuum. They’re directly wiring agentic efficiency into product interfaces and infrastructure to stay competitive. This wave is often compared to the spread of microservices in the past, but we aren’t just adding new dependencies and complexity.