Explanation of AIOps
Artificial Intelligence for IT Operations (AIOps) AIOps tools involve using artificial intelligence and machine learning techniques, as well as big data, data integration, and automation techniques, to make IT operations smarter and more predictive. AIOPS complements manual operations with machine-driven decisions.
AIOps is a collection of best practices, tools, and techniques for deploying and maintaining optimal output from AI models in production. The term AIOps bears a resemblance to another acronym – DevOps – that is also being used in the technology world. Like DevOps, AIOps aims to break down silos and merge different processes. However, unlike DevOps, AIOps is more concerned with the automation of IT services.
Types of AIOps Tools
AIOPS solutions are classified into two categories: 1) domain-centric and 2) domain-agnostic, as defined by Gartner.
Domain-centric solutions implement AIOPS for a certain domain, such as network monitoring, log monitoring, application monitoring, or log collection. You’ll often see monitoring vendors claim AIOps, but primarily they are domain-focused, bringing the power of AI to the domains they manage.
Domain-agnostic solutions operate more broadly and operate across domains, monitoring, logging, cloud, infrastructure, and more. These tools work on a huge amount of IT data from all domains/tools and they derive models from this data to provide more. accurate conclusions and judgments.
Why are AIOps needed?
Many organizations have transitioned from static, disparate on-site systems to a more dynamic mix of on-premises, public cloud, private cloud, and managed cloud environments where resources are continually scaled and reconfigured.
More devices (especially the Internet of Things, or IoT), systems, and applications are providing a tsunami of data that IT needs to monitor. For example, a locomotive can generate a terabyte of data during a single trip. In the language of IT, this explosion is called Big Data.
Traditional IT management solutions cannot keep up with this volume. They cannot sift through events intelligently from a sea of information. They cannot correlate data in interdependent but separate environments. They can’t provide the predictive analytics and real-time insights IT operations need to respond quickly to issues.
Benefits of AIOps
- Improved employee and customer experience
- More efficient use of infrastructure and capacity
- Better alignment with IT services and business service outcomes
- Faster time to deliver new IT services
- Reduced firefighting and avoid costly disruptions
- Better correlation between change and performance
- Improved efficiencies in managing change
- Reduced workload on IT Operations staff because AI is helping with the analysis
- Reduction in false alarms. Faster root cause analysis (RCA) because AI pinpoints the problem or reduces the number of items operators must look at to a small set
- Reducing the skills gap
- Reduction of human error
- Unified view of the IT environment
- Support for traditional infrastructure, public cloud, private cloud, and hybrid cloud
- Moving from reactive to proactive to predictive problem management
- Modernizing IT operations and the IT operations team
- Higher levels of security-to-operations collaboration