Operations | Monitoring | ITSM | DevOps | Cloud

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.

Like all organizations, Elastic deals with an ever-increasing volume of information and data, making it harder for our teams to keep information up to date and for employees to find answers from relevant resources. As a leading Search AI company, our approach to customer-first starts with customer zero — us. When our employees needed a better way to find the information necessary to do their jobs, we knew we could use our own technology to bring that vision to life.

As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of AI and ML fail to reach production.

Learn how to keep your APIs running smoothly! From tracking performance to boosting reliability, this guide has everything developers need.