What You Need to Know About AIOpsBlog home
Currently, problems forming in the network industry are outpacing the solutions being created. A large number of company’s have been forced to utilize high-cost, custom solutions in order to achieve maximum performance and improve individual application.
Artificial intelligence for IT operations, or AIOps for short, is gaining momentum globally as the volume of data grows and systems become more complex. Gartner expects artificial intelligence to be involved in every facet of IT administration, including capacity management, IT service management, cloud platform monitoring, IT infrastructure monitoring, application performance management (APM), the delivery tool chain (CI/CD), and performance management.
Only three years ago, AIOps was just a concept. As of now, Gartner foresees that 25 percent of organizations globally will have begun utilizing AIOps platforms to support two or more large scale IT operations.
Although this technology is still improving, AIOps today can still produce automated suggestions as solutions to difficult, costly issues involved with big data deployments across entire complex stacks. Attempting to coordinate this manually would be impossible without AIOps.
Application performance management for modern data is made possible through AIOps. The reason for this is that AIOps removes the suffering of trial-and-error configuration, remediation workflows, and troubleshooting that have always hindered big data operations teams. DevOps and dataOps teams can even handle large-scale data deployments, remediate issues, support SLAs, and troubleshoot problems in an efficient manner for the first time ever.
It’s a whole new world for modern data centers. AIOps is working hard to help automate and improve performance of big data applications, and to solve the main issues that DevOps faces with the unchecked growth of big data.
There will likely be new obstacles uncovered along the way. For example, a great deal of enterprises are experiencing groundbreaking increases in data volumes. But with DevOps and AIOps working together, facing those issues will be far easier.