GUEST OPINION: Cloud computing has brought countless benefits to organizations of all types, but it’s not without challenges. The cloud resources required to support a large organization can be extremely complex and difficult to manage.
DevOps and site reliability engineering (SRE) teams face challenges in trying to manage people, processes, and data so they can simultaneously meet internal business goals and customer expectations: all while making software in a measurable and efficient way.
Many solutions have emerged that use artificial intelligence to assist in this task, but they do not deliver the need of those charged with developing software applications for cloud environments.
The generic name for such solutions is artificial intelligence for IT operations (AIOps), a term coined by Gartner in 2017. In its blog, Gartner defines AIOps as follows:
“AIOps platforms use big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) with proactive, personal and dynamic “AIOps platforms enable the simultaneous use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”
Modern v traditional AIOps
Traditional AIOps approaches use machine-learning (ML) models to reduce alerts and create dashboards designed to enable issues to be attributed. However, traditional AIOs can be difficult to measure because the underlying ML cannot identify the root of the problems. These ML approaches are also not autonomous: they do not train themselves. They require analysts to refine them by filtering out false positives.
These shortcomings have led to the emergence of a new generation of modern AIOps platforms that better support today’s cloud environments and automated software development. They combine full-stack observability with a deterministic AI engine that can yield accurate, continuous and actionable insights in real time. They support fully automated cloud operations throughout the software development lifecycle.
Modern AIOps solutions can support DevOps-driven software development from start-up to deployment, through automated testing and release validation. They eliminate a lot of manual work, allowing developers to move at a faster speed. Instead of relying on the standard dashboards provided by their AIOps solution, developers can customize their own dashboards, to display the accurate answers they need to perform their role and collaborate with other teams. .
Once production software is deployed, innovative AIOps platforms can help ensure applications are reliable and continue to deliver seamless user experiences. They can even make their applications self-healing, so problems can be solved automatically without human intervention from the DevOps teams.
Boosting developer efficiency
All of these features increase developer efficiency by automating more mundane software deployment and operation tasks. This frees up developers — a valuable resource — to focus on the more creative aspects of software development.
DevOps is greatly accelerated by combining multiple open source solutions into a unified toolchain to solve a particular problem. But even though these solutions are all designed to facilitate and speed up software development, assembling them into a functional whole can consume a large amount of a developer’s time. Modern AIOps solutions can facilitate this process, in addition to increasing software delivery speeds and improving code quality.
AIOps platforms can allow developers to optimize their software more effectively and pinpoint the root of any problems more quickly, in some cases before those issues appear in a production environment. .
Many organizations use a range of IT service management platforms to manage their modern cloud environments, such as ServiceNow, Ansible and PagerDuty. Modern AIOps platforms that support the integration of these solutions can greatly enhance the efficiency and automation of seamless delivery processes.
Application software development is rarely set and forgotten. New functions are constantly needed to meet changing business needs. Sometimes the necessary changes can degrade performance. Modern AIOp can immediately pinpoint this, revert to the previous version if necessary and often pinpoint the root of the problem.
In summary, the differences between traditional and modern AIOps are that newer approaches are dynamic and produce actionable information in real time. They can be customized using user-generated dashboards, allowing teams to work more independently and collaborate more effectively. Ultimately, modern AIOps empower developers to innovate rather than spend time responding and fixing problems.
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