Top AIOps Trends Impacting DevOps in 2020 and Beyond
In this article, see some of the top AIOps trends that are impacting DevOps in 2020 and beyond.
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Over the years, DevOps has gained prominence and never ceased to shape the software world.
It has transformed the way software is delivered to the nearly-limitless marketplace.
DevOps has not only enhanced the pace of software delivery but also facilitated a new form of collaboration among the teams. But the overall cultural, digital transformation has made IT environments more modular, ephemeral, and dynamic.
All these directly reflected in the increased complexity of the new collaborative environment.
At this very juncture, AIOps has gained significance as the most effective technology to aid the DevOps solutions team to monitor and control their applications and underlying infrastructure.
“According to the IDC, the worldwide spend on AI systems will reach USD 77.6 billion in 2022, over three times what it had estimated for 2018.”
What Is AIOps?
AIOps is the amalgamation of artificial intelligence (AI) and operations (Ops). This indicates that it is the application of artificial intelligence to control or manage IT operations.
AIOps helps DevOps teams to enhance key processes, tasks, and decision-making by automating the fast analysis of data.
Machine learning helps in analyzing the data for predictions or alerts leading to better decision-making.
The automation and prediction capabilities associated with AIOps are so promising for the DevOps as the volumes of data generated in IT is growing continually.
How AIOps Will Impact DevOps in 2020
1) High Collaboration
DevOps is all about collaborating the relationships between developers and IT operations to drive value faster. Historically, DevOps teams have been given the freedom to operate independently. Even the individuals within a team can operate independently.
However, in 2020, the DevOps-focused organizations will impose a more disciplined approach with the help of AIOps.
AIOPs facilitates a comprehensive view and cross-team awareness of the overall application performance and infrastructure status. It gathers valuable information and data from all the DevOps monitoring tools, analyses important alerts and detects faults across the CI/CD processes.
This, in turn, eliminates the silos among the DevOps teams, improves cross-team collaboration and quickly detects and resolves the application errors.
Automation is the key principle of DevOps practice. DevOps utilizes lean methodologies that aim at reducing manual operations across development, operations, and finally, consumer segments. It automates all the processes including CI, CD, testing, and application performance monitoring.
In 2020, the DevOps teams will witness the automation of many aspects of code writing. It is all about automating the coders’ task by helping in decision making about flow and operations.
So, with the increasing demand for automation, it's inevitable for the DevOps teams to rely on AI. As the data sets including metrics, logs, and traces are incredibly complex, dynamic, and volatile, the real-time analysis of these data types will require AI and ML methodologies.
At this very juncture, AIOPs technologies come to the aid of DevOps teams to deal with these data sets.
DevSecOps refers to the practice of securing the entire DevOps environment through strategies, policies, and technologies. It aims at securing every part of the DevOps lifecycle including product design, development, testing, delivery, operations, support, maintenance, and more.
DevSecOps ensures that security checks are incorporated and automated throughout the CI/CD pipeline.
AIOps will further entail the embedding governance and cybersecurity functions throughout the DevOps workflow. And, it fortifies the ability of the DevOps teams to deal with development, operations, and security concerns through a single lens.
4) New Skills
In 2020, DevOps teams will leverage the benefits of AIOps which, in turn, automates a number of manual work procedures. This will raise the need for new skills and new job opportunities.
For instance, the dependency on AIOps will require an AIOps Architecture, a developer of meta-algorithms for controlling AIOps platforms.
So, to address this need, the DevOps teams should expand and renew their skills, and organizations should provide appropriate training.
5) Transparent AIOps
The decision-making process becomes hard to understand with the AI becoming more data-driven, intelligent, and change-tolerant. Moreover, most of the algorithms used by AIOps are based in mathematics and statistical theories that are complex for a computer science undergraduate.
Therefore, many IT Ops professionals consider the algorithms as a psychological “Black Box”.
However, in 2020, the DevOps teams will witness the transparent AI algorithms that help in the effective auditing of AIOps systems.
As the growth prospect of the DevOps culture is very high in 2020, its imperative for the DevOps teams to understand the current and future impact of AIOps on their careers, tools, and processes. They should understand how the AIOps will enable DevOps to embrace the scale and speed of modern development.
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