Artificial Intelligence for IT Operations (AIOps) is the application of AI, and related technologies, such as machine learning and natural language processing (NLP) to traditional IT Ops activities and tasks
How Does AIOps Work?
Not all AIOps tools are created equal. To get the most value, an organization should deploy it as an independent platform that ingests data from all IT monitoring sources, and acts as a central system of engagement.
Such a platform must be powered by five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:
- Data selection
Taking the massive amount of highly redundant and noisy IT data generated by a modern IT environment and selecting the data elements that indicate there’s a problem, which often means filtering out up to 99% of this data.
- Pattern discovery
Correlating and finding relationships between the selected, meaningful data elements, and grouping them, for further advanced analytics.
Also called root cause analysis, identifying root causes of problems and recurring issues, so that you can take action on what has been discovered.
Notifying appropriate operators and teams, and facilitating collaboration among them, in particular when individuals are geographically dispersed, as well as preserving data on incidents that can accelerate future diagnosis of similar problems.
Automating response and remediation as much as possible, to make solutions more precise and quick.
In a real world setting, the AIOps platform ingests heterogeneous data from many different sources about all components of the IT environment — networks, applications, infrastructure, cloud instances, storage and more.
- Using algorithms, it removes noise and duplication, and selects only the truly relevant data. This algorithmic filtering massively reduces the number of alerts Ops teams must deal with, and eliminates duplication of work caused by redundant tickets routed to different teams.
- It then groups and correlates this relevant information using various criteria, like text, time and topology. Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events.
- The platform communicates the result of that analysis to a virtual collaborative environment where everyone involved in solving an incident has access to all the relevant data. These virtual teams can be assembled on the fly, enabling different specialists to “swarm” around an issue that spans technological or organizational boundaries.
They can then quickly decide upon fixes, and choose automated responses for fast and precise resolution of the incident. For example, existing ticketing and incident management systems can take advantage of AIOps capabilities, integrating directly into existing processes. AIOps also improves automation of incident response, by enabling workflows to be triggered with or without human intervention.