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Deploying software to support the work of an enterprise is an increasingly complex job that’s often referred to as ‘devops.’ When enterprise teams started using artificial intelligence (AI) algorithms to more efficiently and collaboratively run these operations, end users coined the term AIops for these tasks.
AI can help large software installations by watching the software run and flag any anomalies or instances of poor performance. The software can examine logs and track key metrics, like response time, to evaluate the speed and effectiveness of the code. When the values deviate, the AI can suggest solutions and even implement some of them.
There are several stages to the process:
Detection or observability: The software absorbs as many metrics and event logs as possible. The focus is generally on poor performance that can affect users directly, like a 404 error or an especially long database query run time. Some systems, though, may watch for other issues like a failed sensor or an overheated device. Predictive analytics: After collecting data for some time, AIops software can begin to identify precursors that can often signal an upcoming failure. The AI algorithms are optimized to look for correlations between values, especially those that are anomalies that may indicate upcoming problems. Proactive mitigation: Some AIops algorithms can be tuned to respond immediately to potential problems when the solution is straightforward. For example, a crashing service may be rebooted or reinitialized with more RAM. When these solutions work, they can eliminate much of the problem and save end users from encountering failures. AIops is growing in complexity as teams deploy algorithms to a varie …