WHAT IS AIOPS?
Algorithmic IT Operations (AIOps) is a method of managing and analyzing data from an application using machine learning analytics technology to simplify IT operations management and automate problem resolution.
Why do enterprises use AIOps?
AIOps is designed to automate IT operations and accelerate performance efficiency. IT teams use AIOps to identify trends, detect anomalies, predict future behaviors, and build better processes.
Fundamentally, AIOps cuts through noise and identifies, troubleshoots, and resolves common issues within IT operations. By bringing data together from diverse sources and performing a real-time analysis at source, it helps IT teams better manage incidents, capacity, change, and performance.
As a technology that uses machine learning, AIOps platforms understand and analyze current and historical data, linking anomalies and observed patterns to relevant events. Following that analysis, it initiates appropriate automation-driven action, which can yield uninterrupted improvements and fixes.
How is AIOps used?
On an enterprise level, using AIOps helps businesses acquire insights from the huge volume of data coming from a hyper-connected world so they can better understand their customers and tailor new products and experiences to satisfy them. In addition, with AIOps, an enterprise can increase service levels, reduce or stabilize costs for managing IT environments, and limit risks associated with security and compliance.
AIOps adapts three underlying principles of the DevOps movement—systems thinking, amplifying feedback loops, and creating a culture of continuous experimentation and learning—to IT operations for the improved agility and efficiency that makes DevOps a powerhouse of innovation.
What are the components of AIOps?
Several technologies make up AIOps platforms. These include:
From various IT disciplines, such as events, logs, metrics, tickets, monitoring, and job data, among others.
Tools that can process data in real time, such as Elastic Stack, Hadoop 2.0, and certain Apache technologies.
Rules and patterns
Provide context and reveal data abnormalities and regularities.
Machine learning (ML)
Uses algorithmic analysis to automatically modify existing algorithms or construct new algorithms.
Intelligently comprehend rules and patterns and apply them to accomplish IT-specific goals like correlating unstructured data, removing noise, alerting on irregularities, determining probable causes, and establishing baselines.
Results of machine learning and artificial intelligence are used to automatically build and apply responses to identified issues and scenarios.
Adapts to unfamiliar and new elements.
What are the benefits of AIOps?
Many organizations perform manual monitoring, troubleshooting, and diagnostics of their increasingly complex IT environments, which wastes time and resources. Enterprises deploy AIOps to increase efficiency and reduce costly unexpected system downtime. With this time saved and improved effectiveness, AIOps allow IT teams more time for innovation.
The following are the specific benefits that AIOps offers an organization:
Higher level corporate stability and performance
AIOps systems perform continuous monitoring in the background so staff can address complex issues and tackle higher priority tasks.
Faster analysis and repair
By collecting and clustering different data sources, AIOps systems identify causal risks and initiate repairs to challenging and unexpected issues more quickly.
Better workflows and collaboration
AIOps provide customized reports and dashboards to help teams maintain focus and communicate more effectively during cross-departmental operations.
AIOps eliminates noise and distractions, allowing IT staff to focus on critical issues.
By correlating data across numerous sources, silos are eliminated, and the complete IT infrastructure is observable in one place.
AIOps enables frictionless collaboration, which speeds up diagnosis, analysis, and resolution timeframes.
How does AIOps work?
AIOps works by consuming huge amounts of data directly from IT systems (logs and time series from network, storage, server, and other layers from the enterprise stack), as well as structured data from IT management systems, such as existing infrastructure monitoring tools, application performance and network performance monitoring tools, and IT operations management tools. Essentially, it aggregates the data siloed across IT operations into one place and applies targeted analytics and machine learning to gain deep insights into data patterns.
The machine learning tasks AIOps undertakes are:
Separating out the “noise”
By applying rules and matching patterns, AIOps can sift through your IT operations data and isolate significant abnormal event alerts from everything else.
Identifying root causes and proposing solutions
Within the huge pool of event data, AIOps uses specified algorithms to find abnormal events and connect them with other event data across environments, building intelligence to identify the causes of problems and recommend repairs/solutions.
AIOps processes results from machine learning to send automatic responses that address problems in real time.
AIOps can use the results of its analytics to adapt or develop new algorithms to improve responses and solutions.
AIOps and HPE
- The methodology to build a road map for AIOps use cases driven by business needs and goals
- The design and implementation of a high-performance, scalable, and flexible data platform that can manage the volumes of the different data types, at the right speed for each use case.
- The development and operationalization of machine learning models that can work automatically and in real time using massive amounts of data.
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