Reduce recovery time with AI-enabled troubleshooting
InfoQ Homepage News AI-Enabled Troubleshooting Reduces Recovery Time
Culture & Methods BookmarkNovember 14, 2021 Read time 7 minutes
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Link to the original (Posted on 2021/10/21)
Anomaly detection machine learning algorithms train generalized ML models and apply them to detect hidden patterns and identify suspicious behaviors, helping DevOps in their daily work. Machine learning applied to IT operations (AIOP) is beginning to move from enterprise research environments to production environments.
Florian Schmidt, Postdoctoral Researcher at Technische Universität Berlin, gave a talk at DevOpsCon Berlin 2021 about AI-driven support for log file troubleshooting.
According to Schmidt, companies relying on experts to troubleshoot log files are too costly due to the lack of DevOps/SRE, while their applications are becoming more and more containerized services and functions. Increasing the number of components hosted singly through .
Schmidt explained how machine learning can be used to reduce troubleshooting time:
Machine learning models can detect hidden patterns in log data and identify suspicious behavior, explains Schmidt:
InfoQ interviewed Florian Schmidt about AI-enabled troubleshooting.
InfoQ: What is the real-world situation when using logs to troubleshoot complex applications?
InfoQ: What are the pros and cons of these approaches?
InfoQ: What role can machine learning models play in troubleshooting?
InfoQ: What did you learn?
InfoQ: What do you see in the future for AI applied to troubleshooting?
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