Use of AIOps & ML in Network Operation Centers
An introduction to AIOps: What it is and why it matters
AIOps combines artificial intelligence (AI) and machine learning with IT operations to help IT Ops teams maintain system reliability and availability.
Using artificial intelligence and machine learning, AIOps speeds up the analysis of IT problems and puts incident handling on the fast track to automation.
AIOps was developed in response to alert fatigue and the challenges of achieving service level agreements. The data from disparate IT systems can overwhelm systems administrators and network operators.
As a result of the sheer volume of IT monitoring data, false positives, false negatives, and a wide variety of data types and formats, it becomes nearly impossible to pinpoint the real problems and generate meaningful insight into them.
In addition, multiple dashboards, ticketing systems, and incident-response tools create a backlog of tasks that are required to analyze data and respond to incidents. All of this slows down the meantime to resolution and reduces the number of incidents IT teams can resolve in a week.
How Much Do You Know About NOC Pricing?
How AIOps can benefit your organization and what you need to know.
An overview of AIOps
IT pros use AIOps to automate the analysis of data streaming from IT monitoring tools using machine learning and data science, similar to how BI professionals use analytics and AI to make smarter and faster business decisions.
Combining data collection, data management, and predictive analytics makes AIOps powerful. Based on all the inputs, we can create a single view of the data. One of AIOps’ main value propositions is the contextual analysis of each piece of monitoring data with all the others.
The use of manual methods makes it difficult to achieve this kind of holistic view. As a result, it can be possible to detect and prioritize anomalies that would have otherwise gone unnoticed before larger problems appeared, which can lead to a faster diagnosis. In addition, native capabilities and integration with existing ops tools simplify the process of orchestrating and automating problems after diagnosis.
Examples of AIOps use
AIOps can be categorized into three main capabilities by Gartner analysts:
- Observation: Includes ingesting and correlating data.
- Engage: Identifying root causes through task automation, risk analysis, and knowledge management.
- Act: Includes runbooks and scripts to begin resolving the identified issues.
According to network analysts, few tools can fulfill all of the promises made within these three buckets currently. AIOps, as a class, is evolving to tackle many of the nagging efficiency and strategic problems that IT operations teams confront today.
As the volume of monitoring information grows with the mushrooming of the Internet of Things and edge systems, this includes cutting down on false positives and speeding up the correlation of data.
Additionally, ops teams are using AIOps both to identify hair-on-fire incidents more quickly as well as to engage in preventative maintenance and root-cause troubleshooting. Over time, this will start to pay down technical debt and reduce the number of incidents of the most serious kind.
AIOps is also used by more advanced organizations to improve IT service management, such as by enhancing inquiry management and self-service capabilities.
AIOps in NOCs: Automation and Machine Learning
Automation and machine learning are also improving the effectiveness of NOCs. Nowadays, even when dealing with vast datasets, the software can perform low-risk tasks. As a result, NOC personnel can concentrate on high-value tasks and drill down on issues that the system identifies more easily.
NOC workflows cannot be complete without AIOps. Using it, NOC service providers are mining data faster than humans can and then applying findings to real-life scenarios. Information can be shared in real-time (through APIs, emails, or other methods) and events can be linked together automatically using AIOps tools. In addition to recognizing new patterns over time, machine learning also enables NOC providers to become more familiar with network activity.
IT will be transformed by AIOps
The field of AIOps is still in its infancy, but as momentum builds, experts expect that it will begin to change the landscape of IT operations. In network operations centers (NOCs), fewer people will have to handle alerts and provide first-response assistance.
The IT team will need more people to curate data, train algorithms, design workflows, and create runbooks, and intervene in complex root-cause analysis, preventative maintenance, and architectural design.