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Cloud platforms, managed service providers, and organizations undertaking digital transformations are beginning to reap the benefits of an emerging IT trend: the use of AI-powered IT operations technology to monitor and manage the IT portfolio automatically.
This emerging practice, known as AIOps, is helping enterprises head off potential outages and performance issues before they negatively impact operations, customers, and the bottom line. But the more advanced deployments are beginning to use AI systems not just to identify issues, or to predict issues before they happen, but to react to events with intelligent, automated mitigation.
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But what exactly is AIOps and how are organizations putting it to use today? Here we take a deeper look at the technologies, strategies, and challenges of AI-assisted IT operations.
What is AIOps?
AIOps is an emerging IT practice that applies artificial intelligence to IT operations to help organizations intelligently manage infrastructure, networks, and applications for performance, resilience, capacity, uptime, and, in some cases, security. By shifting traditional, threshold-based alerts and manual processes to systems that take advantage of AI and machine learning, AIOps enables organizations to better monitor IT assets and anticipate negative incidents and impacts before they take hold.
Carhartt CIO John Hill leverages AIOps at the work-apparel retailer in three main areas: service management, performance management, and IT automation. Thanks to intelligent monitoring, Carthartt can now spot problems before they impact users or customers.
"It's the whole process of monitoring your environment and understanding what's going on — and taking actions based on those indicators," he says. "Previously, you would rely on an outage or some indication that something isn't working" to know when a fix was needed — events likely to have already degraded customer experience before you knew of them.
AIOps tools
Many AIOps platforms have been built on monitoring systems with a long history. Others began in AI labs and grew outwards. Good AIOps tools generate forward-looking guesses about machine load and then watch to see whether anything deviates from these estimates. Anomalies might be turned...