AIOps( artificial intelligence for IT operations) is the application of artificial intelligence, machine learning, and big data analytics to simplify and enhance IT operations. It is an umbrella term for the automation of traditional IT Ops activities.
Big data and machine learning are combined in AIOps to produce predicted results that speed up root-cause analysis (RCA) and reduce mean time to repair (MTTR). An organization’s ITOps may continuously improve, saving business resources by delivering intelligent insights that promote a better level of automation and collaboration.
AIOps is the future of IT operation management. It guarantees an uninterrupted user experience with high performance and availability of an application. Uninterrupted user experience is achieved by integrating manual IT operations tools into intelligent and automated IT operation platforms. Hence, enabling IT operations teams to respond quickly to outages and slowdowns with end-to-end visibility.
Today, most organizations are digitally transforming and adopting cloud technologies. Systems and applications across these platforms generate unlimited growing data known as Big data.Big data exceeds the capacity of conventional domain-based IT management solutions. Traditional IT management solutions can only correlate data across some environments. These solutions do not support the predictive analysis that IT operations teams need to handle issues to meet customer service level expectations.
AIOps is the solution that gives visibility into performance and dependencies across all environments. It analyses the data to extract critical events related to slowdowns or outages. It automatically notifies IT staff of issues, their causes, and recommended solutions.
Figure 1 What is AIOps?
The best way to understand how AIOps work is to examine the roles played by the component technologies—big data and machine learning. Artificial intelligence for IT operations follows five dimensions of IT Ops monitoring. The five key dimensions are;
AIOps-I Data Selection
AIOps uses big data platforms to aggregate redundant and noisy IT data in a single place. The data include the following:
AIOps-II Pattern Discovery
In this phase, analytics like rule application and pattern matching are performed to select meaningful data elements from noise.
AIOps-III Inference
Industry-specific algorithms are used to identify the problem’s root cause. Strange events are correlated with the other events data across environments to determine the cause of a performance or an outage problem.
AIOps-IV Collaboration
AIOps automatically sends alerts and problem solutions to the concerned IT teams or assembles response teams based on the problem and its solution.
AIOps-V Automation
AIOps use machine learning results to trigger automatic system responses that deal with issues in real time, sometimes before users are even aware they exist.
Figure 2 How does AIOps work?
AIOps makes it possible for IT operations to detect, address, and fix slowdowns and outages more quickly than manually filtering through alerts from diverse IT operations tools. Other advantages are listed below:
Improve operational confidence
IT operations technologies are no longer simply “IT”. From foundational security to more advanced automation, AIOps aims at eliminating manual processes through machine learning. AIOps improve efficiency by taking the guesswork out of many IT operations processes with real-time insight into potential issues and providing step-by-step solution guidance.
Continually manage vulnerability risks.
As the environment grows in size and complexity, organizations find that manual methods cannot keep up with the rate of change. By introducing AIOps solutions into the organization, companies can reduce risk and make decisions faster, staying ahead of tomorrow’s challenges.
Control complexity
IT teams can benefit from AIOps by reducing operational complexity, enhancing reliability, and streamlining patch and configuration management for systems.
Optimize skills and resources
By providing root cause analysis and solution guidance, AI operations can help teams solve problems more efficiently while deepening their understanding and skills.
Achieve faster mean time to resolution (MTTR)
AIOps can correlate data from multiple IT environments to identify root causes and propose solutions faster and more accurately. Thus enabling companies to improve the MTTR significantly.
For example, telecommunications provider Nextel Brazil reduced incident response times from 30 minutes to less than 5 minutes.
AIOps is a cloud-based solution that turns data from multiple IT environments into actionable insights that accelerate responsiveness to problems, lower costs, and boost staff performance. However, AIOps may also have challenges in terms of implementation.
Data: Modern IT operations data can be overwhelming to wrangle into formats that support machine learning models. Modeling data from multiple sources, cleaning and normalizing it, and then integrating it into one database can be a lot for IT teams to handle.
Infrastructure: AIOps technology is expensive, and specialized infrastructure and deployments are needed.
Expertise: AIOps requires data science expertise to build intelligent, sustainable solutions.
AIOps use cases
AIOps visibility and automation can assist and help drive other significant business and IT goals in addition to enhancing IT operations:
DevOps Adoption
DevOps is a powerful and disruptive approach to software development that gives development teams more power to provision and reconfigure infrastructure, but IT still has to manage it. AIOps provides the visibility and automation needed to support DevOps without additional management effort.
Digital Transformation
Organizations can transition to digital transformation with more freedom and flexibility to meet strategic business goals.
Cloud Migration
Most organizations are transitioning to cloud solutions using a hybrid approach. With AIOps, organizations can easily track and gain visibility into a multi-cloud environment. It helps organizations identify any risks and dependencies in a clear, unified manner.
The AIOps approach is about making better decisions faster. Find and fix problems before you’re aware of them. ABC AIOps Team can help you focus on doing what’s essential for your business. With Arcana’s approach to automation and optimization, you can cut down on costs and spend more time doing what’s necessary. We are here to help you optimize your IT for growth.
Platform as a Service is a cloud development and deployment environment with the resources organizations need to deliver anything from basic cloud-based apps to complex enterprise applications.
A hybrid cloud is a type of cloud computing environment that combines public and private cloud resources. The primary benefit of this type of configuration is the ability to provide access to the services of both public and private.
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