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DevOps with Artificial Intelligence

 

With orchestration and monitoring playing such key roles in DevOps, the emerging trend of using artificial intelligence (AI) to support and even automate operations roles by delivering real-time insights about what’s happening in your infrastructure seems an obvious fit.

DevOps is about improving agility and flexibility; AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. That’s especially true as trends like microservices, hybrid cloud, edge computing and IoT increase the complexity of app infrastructures — and the number of logs that you might have to look at to find the root cause of an issue, and the number of people who need to be in a conference call or chat room tracking down what’s gone wrong and how to fix it.

AIOps depends on aggregating data from multiple systems and DevOps relies on integrating previously siloed systems. AIOps requires the same kind of culture change as DevOps because it means looking at the entire system rather than specific technologies or infrastructure layers, and being comfortable with a high level of automation.

The promise of AIOps is that it can detect anomalies, predict performance problems and deviations from the baseline, suggest optimizations, correlate signals across multiple platforms for troubleshooting, do root cause analysis and even automate fixes if you’re comfortable with that.

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