The digital transformation of companies has led to the creation of an IT infrastructure that includes gigantic data warehouses and hybrid and multi-cloud systems. Development has led to the generation of huge data sets from multiple channels, customer touch points, and device platforms. The unrivaled pace of data generation makes it difficult for organizations to manage IT, which is critical to streamline operations, improve monitoring, and pursue business continuity. Given the limitations of existing IT solutions for managing data, companies are leveraging AIOps to perform a myriad of activities. These include understanding and predicting customer behavior, detecting anomalies and determining their reasons, and offering prescriptive advice. Helps detect dependencies responsible for creating problems in an IT infrastructure. Also, since artificial intelligence has features like containerization, continuous monitoring, predictive or adaptive cloud management, companies can get a next-generation perspective on their business.
What is AIOps?
It is a software system that comprises big data, machine learning and artificial intelligence to improve the capacity and operation of all primary IT functions. IT functions can include automation, IT service management, performance monitoring, and event correlation and analysis, among others. In other words, AIOps is applying data science and machine learning to the DevOps framework to make it more efficient and productive. The benefits of integrating AI into the value chain are:
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Fast and accurate processing of all types of data generated from various sources. This results in ensuring data integrity and achieving tangible results.
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Analyze huge data sets to generate actionable insights for DevOps engineers to understand and make adjustments to the infrastructure (if necessary).
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Identify event patterns and configure automatic triggers in response.
AIOps vs DevOps: the difference
DevOps is arguably the best software development methodology that increases the speed of implementation of quality software solutions in any organization. So why has AIOps become a crucial requirement for businesses? Let’s find out.
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The main difference between AIOps and DevOps is the multi-layered formation of the former that can automate IT operations and enable algorithmic analysis on its own. On the other hand, DevOps transformation involves leveraging agile development methodologies and using them to automate self-service operations.
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AIOps executes tasks in real time without human intervention. You can analyze and organize IT tasks based on data sources, which traditional DevOps cannot understand, much less execute.
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AIOps can perform a number of data-driven analytical activities, such as managing streaming data, managing historical data, ingesting log data, and more. You can enable stakeholders from multiple business units to view insights by taking advantage of visualization capabilities.
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Although DevOps QA can automate build deployment using containers and automation tools, it lacks areas such as security and compliance, and system operations.
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DevOps QA helps optimize SDLC across CI / CD pipelines, while AIOps offers a scalable platform to automate and manage IT operations involving huge data sets.
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The importance of AIOps will increase in the coming days, as next-generation business applications running in multiple cloud ecosystems will need to be monitored and managed in real time.
Why should companies adopt AIOps?
Building and deploying next-generation business applications would involve the use of AIOps methodology powered by artificial intelligence and machine learning. The benefits of taking advantage of this next generation methodology are:
Eliminate IT noise: IT noise can expose computers to false positives, hide root cause events, and make outages difficult to detect. It can also lead to performance issues, increased operational costs and risks, and override of business digital initiatives. AIOps-powered tools can reduce or even eliminate noise by creating correlated incidents that point to the root cause.
Superior customer experience: With customer experience becoming the most crucial factor in driving profitability, AIOps can perform predictive analytics and automate decisions related to future events. By analyzing the data, AIOps can predict events that impact the availability and performance of IT systems. Plus, by identifying the root cause of IT problems, you can help solve them instantly.
Best collaboration: AIOps can break down functional silos and streamline workflow for IT groups and other business units. You can generate custom dashboards and reports so teams quickly understand and act on their tasks.
Improve service delivery: Artificial intelligence, machine learning, and automation can assist any company’s service delivery team in query resolution by analyzing usage patterns, support tickets, and user interaction. By applying probable cause analysis, you can predict underlying performance problems and help resolve them.
Conclution
Although DevOps test automation is the de facto standard for enabling automation of IT processes, AIOps can be an entirely different ball game. You can take over from DevOps as your next-generation avatar by minimizing companies’ reliance on specific automation tools. Additionally, AIOps can monitor the behavior of the IT infrastructure, and by aligning data resources, it can streamline work processes and drive profitability.