, where artificial intelligence and machine learning automate tasks to augment technology operations teams, businesses undergo a monumental shift that enables them to be more proactive, predictive and ultimately preemptive.Along this journey, the skills needed in your ITOps, DevOps, and site reliability engineering teams will also evolve, requiring skills in customization, integration, automation, and governance. Most organizations aren't ready for this seismic shift, however.
You may notice that RelEng focuses on SDLC tasks such as automating builds, tests and QA— essentially automating all the work for deploying an application into production. In the past, DevOps failed to pay equal attention to the operations effort needed during the production lifespan of an application. AIOps addresses this DevOps weakness by applying AI/ML to anomaly detection, root cause analysis, resolution and verification, and by driving automation of anomaly resolutions.
Understanding: The ability to understand where the technology or practice is used in the enterprise, and which team to contact if anything needs to be done with it. Automating: Defining, creating and deploying automated processes for the technology or practice, allowing automatic resolution of anomalies by AIOps.
AppDynamics The headline might sound better if you say how does AIOps impact your money?
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