In the production environment, machine learning and AIOps are the new big things to talk about. They're the latest trend for artificial intelligence, another way to build intelligent applications, and often more hype than substance.
AIOps is an emerging software method that attempts to reduce the complexity of software failures, focusing on three fundamental principles:
- Problem Discovery
- Task Prediction
- Task Execution
According to data provided by ISVs, 57 percent of organizations use machine learning, and 86 percent use artificial intelligence to improve business outcomes. Why? Because AI and ML have fundamentally changed how organizations have had to look at their machines, applications, and data, and forced them to rethink how they operate. To make these efficiencies, application owners need to optimize workflows to process data with intelligent analysis to allow insight into the system.
You also need AI and ML to understand user and customer needs to build software that understands what customers want and the need to move more quickly to develop and deliver that software. There's a lot of hype about the term AIOps. It's pretty common to hear it talked about as "AI for IT." Unfortunately, most of the people discussing the word don't understand what it means.
Download the AIOps Guide to get an overview of AIOps, ways you and your organization can get started with it, use of AIOps in DevOps, tools that can be used, and even get to know the learning path for becoming an AIOps Expert. So grab your copy of the AIOps guide right away, and get ahead in your career now!
