Operations | Monitoring | ITSM | DevOps | Cloud

This recurring blog will look at specific ITOM technologies within the OpsRamp solution and general ITOM challenges. Got an idea for this blog or want to learn more about ITOM at OpsRamp?

As per a survey by Comcast Business, around 85% of IT leaders trust AI networking tools for meeting their organization’s goals. This stat alone is enough to show how big of a role AI is playing in network monitoring. And it’s just the beginning, with rapid development in Artificial Intelligence, we might see a lot more sophisticated AI use cases for network monitoring. But how exactly does AI help in network monitoring? What its roles, benefits, challenges, and how to implement it?

In this conversation, Cribl’s Carley Rosato talks to Aflac’s Shawn Cannon about his role as a Threat Management Consultant, and how he manages their SIEM environment, brings in new data as needed, and works to improve the ingestion process. Our customers are always coming up with new and exciting ways to implement Cribl tools — importing a 34 million-row CSV file into Redis and enriching events in Splunk might be one of the most impressive we’ve seen so far.

Monitoring distributed systems means collecting data from various sources, including servers, containers, and applications. In large organizations, this data distribution makes it harder to get a single view of the performance of their entire system. OpenTelemetry helps you streamline your full-stack observability efforts by giving you a single, universal format for collecting and sending telemetry data. Thus, OpenTelemetry makes improving performance and troubleshooting issues easier for teams.

Large Language Models (LLMs) are advanced artificial intelligence models designed to comprehend and generate human-like language. With millions or even billions of [parameters, these models, like GPT-3, excel in natural language processing, understanding context, and generating coherent and contextually relevant text across various applications.