AIOps looms large as a way to help push the DevOps envelope. How can organizations prepare?

AIOps: Supporting Reliability at DevOps Speeds

AIOps – As organizations journey down the path of DevOps maturation, sustainable IT operations and IT service management remains a challenge for many. Even advanced organizations that have managed to speed up deployment rates and improve software quality struggle to maintain the resilience of the infrastructure that supports those applications. To support DevOps speeds, a growing number of organizations are turning to AIOps—the use of artificial intelligence (AI) and machine learning in IT ops—to speed up analysis of IT problems and better automate incident handling.

A new study out this week from OpsRamp shows that ops pros are able to reduce mean-time-to-resolution of incidents by as much as 50% through the use of AIOps. The use of AIOps is still infrequent, but analysts say its prevalence will grow quickly. Gartner says about 5% of organizations were using AIOps tools last year, but that by 2023 the deployment rate will shoot up to 23%.

But as DevOps teams lean on AIOps tools, management will need to harness a new set of skills to get the most out of the types of operational automation AIOps will afford. In the coming years, experts believe AIOps will change the complexion of the IT ops workforce. Fewer people will be needed in the network operations center (NOC) to manually sift through alerts and respond immediately to incidents. More people will be needed to curate data, scrub it and train the algorithms that will do the heavy lifting of event correlation and determining the root cause of problems.

As organizations and individuals plan out how they’re going to future-proof themselves for the changes in IT ops wrought by AIOps, experts say they should increasingly see the need for the following three roles in IT organizations.

This article originally appeared on devops.com To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.