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This article introduces the relevant knowledge of "what is the working principle of AIOps". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Jason English, chief analyst at Intellyx, has said that the challenges of complexity, ultra-high-speed delivery and automation posed by hybrid IT have created an insurmountable storm of events and warnings. Although the emerging AIOps platform is far from perfect, it has been able to provide important help for site reliability engineers (SRE), operators and developers to cope with the storm.

"these AIOps tools are all about data," David Lithicum wrote in GIgaOm's "Key Criteria for AIOps" report. " Lithicum stressed that in the system monitoring process, the real problem can always be exposed is the data. For solutions dedicated to predicting failures or other potential problems / trends, all AI systems must be highly dependent on the data supply during the model training phase.

So how does AIOps work? How do machine learning and human (or application) intelligence use data to help busy SRE and DevOps teams optimize troubleshooting and solve practical problems? Let's talk about it together.

Let's take a look at a few basic definitions.

What is AI? Artificial intelligence (AI) belongs to the general name of the technology that uses machines to simulate human intelligence, and it is by no means as scary as people think. The goal of AI technology is very simple-to enable software to learn, react, develop, identify and automate.

What is machine learning? Machine learning (ML) algorithms are trained on data sets. These algorithms can adjust themselves through experience and "learning" to improve the output. Machine learning algorithms can often find unknown values, patterns and connections from data that human beings will never be aware of. For example, in AIOps, machine learning can significantly enhance event response. Machine learning is a subset of the definition of artificial intelligence.

How does AIOps work?

To understand how AIOps works, let's first look at an example that most development teams may be familiar with.

In today's highly complex systems, countless teams are often quickly submerged in unknown variables and alarm noise. Developers and engineers are stuck in the information quagmire again and again, and it is almost impossible to go through every alarm and every event one by one. The resulting alarm fatigue also causes real urgent alarms to be buried and ignored.

It is impossible for us to deploy a good engineer with 20 years of experience to screen the alarm content full-time, which is a serious waste of talent. At this point, it's AIOps's turn.

AIOps is a new tool that brings the powerful capabilities of AI and machine learning to telemetry data to help teams quickly evaluate data content, respond to actions, and reduce human labor requirements.

In short, AIOps is mainly responsible for data intelligence and data enrichment. It cannot replace the developer role; instead, it is to save valuable time, improve the observability of information, and ultimately help developers create a more perfect product.

Differences between AIOps and other monitoring tools

AIOps can provide rich insights and automation support to DevOps and site reliability engineering teams to help them quickly identify and solve problems.

The existence of intelligent elements is the core difference between AIOps platform and other monitoring tools. It is this key factor that enables AIOps to play an important role in modern work scenarios.

Most enterprises have realized the rapid increase in the complexity of their production systems. In addition, the great wealth of software features also unleashes new growth opportunities and begins to play a more important role in enhancing the customer experience and suppressing competitors. To this end, developers have to bear tremendous pressure to deploy software without error in a record short time to quickly resolve future events.

Machine learning and AI can provide necessary support to standby teams to help them identify, prioritize, and quickly implement troubleshooting and remediation in a fast-paced environment. The AIOps platform also enhances the operation of the existing incident management team and workflow, shortens the average resolution time (MTTR), reduces the amount of labor, and ultimately brings a better experience to employees and end users.

AIOps in practice

The value of AIOps is certainly not limited to noise screening. Here are three possible ways for the AIOps tool to enhance the event response process using AI, machine learning, and automation technologies:

First, active anomaly detection: AIOps tools can automatically detect anomalies in the environment and trigger other monitoring solutions and team collaboration tools, such as Slack, to help developers find unknown variables.

Second, event correlation and enrichment: the AIOps tool can associate relevant alerts and events with corresponding priorities to help us quickly focus on the core issues; in addition, AIOps can also enrich alerts and events with historical data or contextual information from other tools on the stack to guide the team to efficiently discover the root causes. At present, the most advanced AIOps tools have been able to use machine-generated, time-based clustering, similarity algorithms and other machine learning models to enhance the logic of artificially generated decisions to help users automatically eliminate abnormal noise or low-priority alerts.

Third, intelligent alarm and notification: the AIOps tool can automatically route event data to the appropriate event response individual or team, thus saving valuable time. Especially for the distributed self-service teams, this approach can greatly reduce the number of noise alarms received by members, speed up the data transmission efficiency of key events, and ultimately reduce the workload.

The AIOps tool runs machine learning to evaluate the data in the event management and monitoring tool and transfers the problem to the appropriate functional individual / team or specialized technical expert based on a similar situation in the past.

Summary

Actively embracing AIOps,SRE and DevOps teams is expected to gain a deeper understanding of the root causes of problems, quickly alleviate them, reduce alarm fatigue, and ensure that the team can focus on the most valuable work-creative and strategic thinking.

This is the end of "how AIOps works". Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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