AIOps or Artificial Intelligence for IT operations means applying artificial intelligence (AI) to improve IT operational effectiveness. AIOps makes use of aspects like analytics, big data, and machine learning abilities to perform its functions like –

• Gathering and aggregating large and ever-increasing amounts of operations data created by several IT infrastructure components, performance-monitoring tools, and applications.
• Intelligently zeroing in on the ‘signals’ in all that ‘noise’ to categorize important patterns and events associated with the availability issues and system performance.
• Diagnosing root causes and reporting them to the IT section for swift response and recovery actions. In some cases, it helps to resolve these issues automatically without any need for human intervention.
• Enabling IT operations teams to react rapidly by replacing several individual, manual IT operations tools with one intelligent and automated IT operations platform. It also helps to avoid slowdowns and outages proactively, without effort.

Many experts believe that AIOps will become the future of overall IT operations management.

The Need for AIOps

Nowadays, several organizations are abandoning the traditional infrastructure consisting of individual, static physical systems. Today, it’s all about a dynamic combination of on-premise, managed, private, and public cloud settings. They prefer running on virtualized or software-oriented resources that upgrade and reconfigure continually.

Various systems and applications across these environments create an ever-rising tidal wave of operational data. The average enterprise IT infrastructure, as estimated by Gartner, produces three-times extra IT operations data annually.

Traditional domain-based IT management solutions can be brought to their knees by volume of data. Intelligently sorting the important events out of the mountain of data is a dream, at best. Correlating data through various but interdependent environments is out of the question. Adding to that, providing predictive analysis and real-time insight for IT operations teams and enabling them to respond to issues promptly, becomes unrealistic. Then, we could wave goodbye to meeting user and customer service level expectations.

With AIOps, you can secure deep visibility into data performance and dependencies through various environments through a unifying solution. You can analyze the data and parse out significant events associated with outages or slowdowns. It can automatically alert IT staff to the issues, their origin and suggest actionable solutions.

How does AIOps work?

The easiest way to understand the working of AIOps is by reviewing the role played by each AIOps component. It includes machine learning, big data, and automation in the operational process.

AIOps makes use of big data platforms to combine siloed IT operations data. This includes:

• System logs and metrics
• Historical performance and event data
• Streaming real-time operations events
• Incident-related data and ticketing
• Network data, including packet data
• Related document-based data

AIOps then taps focused on machine learning and analytics capabilities.

• Individual important event alerts from the ‘noise’: AIOps applies analytics like pattern matching and rule application to sift through the IT operations data and individual signals that denote any important anomalous event alerts.
• Recognize the origin of the issues and suggest solutions: By utilizing environment-specific or industry-specific algorithms, AIOps can compare abnormal events with other event data from all the environments to pinpoint the reason for any performance or outage problem and propose apt remedies.
• Automate responses together with actual proactive resolution: AIOps can route alerts automatically and suggest solutions to the right IT teams. It can also generate response teams depending on the problem’s nature and the solution. In several instances, it can process the results from machine learning to activate automatic system responses. It can address the problems happening in real-time, even before the users become aware of their occurrence.
• Learn always to improve future managing problems: Depending on the machine learning capabilities, analytics AIOps can alter algorithms or develop new ones to recognize problems before occurrence and propose practical solutions. AI models can also support the system to learn about and become accustomed to environment changes, like a new infrastructure installed or reconfigured by DevOps.

Benefits of AIOps

The all-encompassing benefit of AIOps is that it allows IT operations to detect, address, and resolve outages and slowdowns quicker than manually through alerts from several IT operations tools. It results in quite a few benefits, such as –

• Attain faster mean time to resolution (MTTR): AIOps can identify the root causes of problems earlier and more precisely than humanly possible. It helps the organizations to fix and attain ambitious MTTR goals. For instance, Nextel Brazil, a telecommunications service provider, could minimize incident response times from 30 minutes to 5 minutes with AIOps.
• Moving from responsive to proactive to prognostic management: AIOps keeps on learning and better detects less-urgent signals or alerts as opposed to more-urgent circumstances. It can offer predictive alerts that allow the IT teams to address impending problems before they cause outages or slowdowns.
• Streamline IT operations and IT teams: As an alternative to being buried under every alert from every environment setting, only alerts that meet particular service level thresholds or parameters can be sent to AIOps operations teams. It carries the full context necessary for the team to decide on the best possible diagnosis and carry out the fastest corrective measure. As AIOps keeps on learning, improving, and automating, it results in more efficiency with less human effort. Your IT operations team can concentrate on tasks that bring immense strategic value to the business.

AIOps Use-Cases

On top of optimizing IT operations, the visibility and automation support offered by AIOps can help drive other vital aspects of business and IT initiatives. Some of its use cases are as follows –

• Digital transformation: AIOps is designed to handle complex digital transformation in IT operations. It encompasses virtualized resources, multiple environments, and dynamic infrastructure. This enables freedom and flexibility.
• Cloud adoption or migration: Cloud adoption is a gradual process. The norm is a hybrid and multi-cloud setup with several interdependencies that can alter too frequently and quickly to document. AIOps can radically decrease the operational risks by offering a clear vision of the interdependencies in cloud migration in such situations.
• DevOps adoption: DevOps drives development forward by offering more power to setting up and reconfiguring infrastructure for the development teams. However, IT still has to tackle that infrastructure. AIOps offers the necessary automation support to DevOps for effortless management.

AIOps promises to decouple organizational ambitions from the management headache imposed by ballooning IT Infrastructure. This intelligent, automated, and optimized approach to managing the IT backbone could well become an enterprise technology mainstay soon.

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