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AIOps: The future of DevOps

ITGix Team
ITGix Team
Passionate DevOps & Cloud Engineers
15.02.2023
Reading time: 5 mins.
Last Updated: 10.01.2024

Table of Contents

Before we start discussing AIOps and how it can be integrated into DevOps, let me first go back to 2017, when the term “Artificial Intelligence for IT Operations (AIOps)” was defined for the first time by Gartner.

What does “AIOps” mean?

According to Gartner, “AIOps,” or “Artificial Intelligence for IT Operations,” is a term used to describe the use of artificial intelligence and machine learning techniques in the field of IT operations. It refers to the integration of these technologies into the IT operations process in order to automate tasks, improve efficiency, and enhance overall performance.

The heart of AIOps, as depicted in the diagram below, is centered around Big Data and Machine Learning (ML), which are both essential components for implementing AIOps successfully in your organization. The three main steps of the AIOps process are to observe, engage, and act as every effort is related to the exact scope of actions and expectations.

AIOps model Machine Learning, Big Data, and segregation

How can a DevOps Team Take Advantage of AI?

In the context of DevOps, AIOps can be used to improve several key areas of the software development life cycle (SDLC) and delivery process. For example, it can be used to automate the monitoring and management of IT systems, such as servers, networks, and applications. This can include things like identifying and resolving issues in real-time, predicting potential problems before they occur and optimizing resource allocation.

AIOps can also be used to improve the efficiency of incident management, by quickly identifying the root cause of an issue and providing suggested solutions. Additionally, it can be used to optimize the performance of applications and services, by analyzing usage patterns and identifying areas for improvement.

Another key area where AIOps can be applied in the DevOps infinity model is in the realm of testing and quality assurance. Machine learning algorithms can automate testing processes, such as regression testing, and identify application areas that are most likely to contain bugs. Additionally, AI can be used to analyze user feedback and identify patterns in customer complaints, which can help improve the quality of the end product.

What are AIOps’s capabilities and benefits?

One of the key benefits of using AIOps in DevOps is improving overall efficiency and productivity. By automating tasks and identifying issues before they occur, teams can save time and resources that can be reallocated to more strategic initiatives. 

Additionally, AIOps can help reduce downtime and improve overall system availability, which can translate into cost savings for organizations. Other capabilities and benefits are:

  • Reduces events noise;
  • Detects and predicts anomaly /incident early;
  • Malware traffic detection;
  • Reduces IT Operations cost;
  • Decreases MTTR drastically even with a negative sign;
  • Historical analysis;
  • Reduces unplanned downtime;
  • Identify the Root cause;
  • Automates Incident Response;
  • Single System of engagement;
  • The Ops team focuses on innovation instead of repeatable and manual actions;
  • Control complexity;
  • Better customer experience and satisfaction.

What are the business benefits of AIOps?

AIOps has clear business benefits. The massive increase in AIOps’ adoption reflects directly into revolutionizing of IT operations. You can expect to see benefits in the following areas:

  • Breaking Down Data Silos Gaps;
  • Eliminating IT Operational Noise;
  • Increase customer experience and satisfaction;
  • Overcoming Monitoring and Analytics Challenges;
  • Improved business Return Of Investments (ROI) IT productivity.

What is the AIOps platform and how many types have?

According to Gartner’s definition: “AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

Types of AIOps Platforms:

  • Domain Centric – Domain-centric AIOps solutions are mostly focused on bringing AI-driven decisions to a certain domain – typically in the monitoring spaces – like Application Monitoring, Infrastructure Monitoring, Network monitoring, etc.;
  • Domain Agnostic – Domain agnostic AIOps solution ingests data from multiple IT domains and disparate sources, including historical and real-time streaming, and provides cross-domain correlation and provide the highest value in AIOps.
Domain Centric and Domain Agnostic AIOps

The most popular AIOps platforms by type are as follows (keep in mind that this list is relevant to the time when I am writing this blog, and this area is continuously changing and evolving):

Domain Agnostic platform type:

  • BigPanda;
  • Moogsoft;
  • CloudFabrix.

Domain-Centric platform type:

  • Splunk;
  • Datadog;
  • Instana;
  • Dynatrace;
  • AppDynamics;
  • Pagerduty;
  • DRYiCE.

What are the challenges of implementing AIOps into DevOps?

Despite all the above-mentioned benefits, implementing AIOps in DevOps doesn’t come without its challenges. One of the biggest ones is ensuring that the data being used to train the machine learning models is accurate and of high quality. Additionally, there is a need for highly-skilled individuals who can design, implement and maintain the AIOps system.

Maybe you’ve already heard the phrase “Data is the new oil” and you would agree with that, especially from AIOps perspective. 

You may also face several more obstacles on the journey of successfully implementing AIOps into DevOps:

  • Expertise: there’s an intimidating barrier to entry because extensive data science expertise is required;
  • Infrastructure: expensive and specialized infrastructure and deployments are needed;
  • Time-to-value: AIOps systems can be difficult to design, implement, deploy, and manage, so it can take some time to see any return on investment;
  • Data: the volume, quality, and consistency of data produced by modern IT operations can be overwhelming and difficult to wrangle into something that can be used for modeling.

Conclusion 

AIOPS is an emerging field that has the potential to transform the way IT operations are managed and performed. By integrating artificial intelligence (AI) and machine learning (ML) into the DevOps process, organizations can improve efficiency, reduce downtime, and enhance overall performance. However, implementing AIOps in DevOps requires careful consideration and planning to ensure success.

Another essential aspect to consider is the ethical implications that can arise from using AI in IT operations. As machines begin to make more decisions on their own, it’s important to ensure that these decisions are aligned with the goals and values of the organization.

Nonetheless, the future is already here …

AIOps: The future for DevOps …. Not if, but When? Is your team using AIOps in DevOps? If it’s not now, it will be soon.

Tihomir Gramov, DevOps and Cloud Engineer

Keep your eyes open and stay tuned for more AIOps topics on our blog page.

Author:

Tihomir Gramov, DevOps and Cloud Engineer

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