Operations | Monitoring | ITSM | DevOps | Cloud

I’ve been quite open about the many ways AI-driven projects can go wrong. With so many ways to fail, what makes Sumo Logic Mo Copilot set up for success? Let’s discuss each of these points in detail.

With all the hype surrounding AI, it’s critical to focus on building resilient networks capable of handling the performance demands that AI will introduce. As I often say, if your network observability solution isn’t detecting packet loss, neither will your AI engine. When you ask, “What’s the status of our global network health this morning?” a flawed or incomplete response could jeopardize critical decisions.

Given that 100% of the databases I have interacted with in my professional career have been SQL databases, my data-based mental model (please enjoy my pun) has always defaulted to a relational one. However, when spinning up a tiny side project in 2020 (a bot to provide interactivity to my Twitch stream), my data-storing requirements didn’t call for a relational model at the time, so I chose a NoSQL solution: MongoDB.

Another day, another AI headline designed to spike your blood pressure. The Service Management industry is no stranger to dramatic proclamations about AI, and neither are you. But what’s the real story behind CNBC’s latest take, “The tech support desk at work is one of the first jobs AI is rapidly replacing?” Does the Palo Alto Networks company need media coverage? Does CNBC? CompTIA? All of them? Perhaps there is a hidden agenda behind the article being published.

LLM observability is key to ensuring model performance. Learn its importance, best practices, and actionable steps for optimal results and reliability.

In cloud-native environments, observability is key to ensuring the health, performance, and stability of distributed systems. Observability helps developers and operations teams understand how their systems behave in real time, helping diagnose issues, optimize performance, and meet service-level agreements.