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Introduction to AIOps — IT Operations that Detect Failures First with Data and Artificial Intelligence

Learn practical AIOps methodologies to move beyond alarm storms and repetitive incident responses toward data-driven problem prediction and automation. This is a practice-oriented course infused with 20 years of operational experience, covering everything from data preparation and anomaly detection to root cause analysis that can be applied immediately in the field without being tied to a specific product.

19 learners are taking this course

Level Beginner

Course period Unlimited

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What you will gain after the course

  • How to classify and prioritize large volumes of events in complex infrastructure environments based on data

  • Machine learning-based anomaly detection and log analysis for failure prediction and root cause analysis processes

  • AIOps Pipeline Design and Step-by-Step Implementation Strategy Integratable with Existing Monitoring Tools

[ What you will learn ]

· Explaining the difference between AIOps and traditional monitoring in your own words · Designing a workflow that connects metrics, logs, and trace data into a single stream · Selecting and tuning anomaly detection methods that go beyond fixed thresholds · Reducing alert fatigue by 88% by grouping flooding alarms into single incidents · 5 steps for safely implementing automated remediation and designing safety guards · Creating a 90-day implementation roadmap and methods to prove performance with numbers

[ Course Introduction ]

The number of dashboards has increased, yet users are still the ones who notify us of failures first

Back when we operated a single physical server, there were about a hundred events per day. How about now? With the transition to containers and microservices, daily events have reached the tens of thousands. That is a 175-fold increase.

However, the processing capacity of the person viewing them remains the same. The limit for alarms that a single person can seriously evaluate is only a few dozen per day. It is impossible to bridge that gap simply by hiring more people.

So what happened? They mute the alarm channels. They raise the thresholds. Eventually, they miss the real alarms when they actually go off.

This course covers how to overcome that situation using data and algorithms

The term AIOps is heard frequently these days, but related materials are generally divided into two categories: marketing materials focused on product introductions, or algorithmic explanations at a research paper level. In between, there is a lack of resources that tell on-site operators exactly what to start doing next Monday.

I created this lecture to fill that gap. It covers design principles rather than how to use a specific product, and instead of just listing theories, it follows actual failure cases in chronological order.

I will first tell you the reasons why it fails

The reason most AIOps projects I've seen fail wasn't because of the technology. It was the sequence.

If you introduce expensive products before your data is ready, you'll be left with nothing but a pretty screen. If you start with machine learning from the beginning, no one will trust it because you won't be able to explain why something was flagged as an anomaly. If you try to apply it to the entire system at once, the organization will lose interest before the first results even emerge.

This lecture points out those pitfalls one by one and suggests a realistic sequence starting from a statistical foundation. In practice, 80% of the detections actually needed in the field can be resolved using only moving averages and standard deviations.

Recommended for
these people

Who is this course right for?

  • DevOps engineers and SREs who have experience missing critical incidents due to alert fatigue.

  • Infrastructure operations teams that have monitoring tools but fail to utilize data effectively

  • System administrators considering operations automation in container and microservices environments

Need to know before starting?

  • Experience with basic Linux commands and checking system logs

  • Experience using monitoring tools (Prometheus, Grafana, etc.) or understanding of basic concepts

  • Basic Python syntax and the ability to write simple scripts

Hello
This is jjangkbg7719

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Hello, I'm Frank.

I am an infrastructure engineer who has been wrestling with servers for over 20 years. Starting with Linux server operations, I am now in charge of the cloud infrastructure for a large-scale payment service. My daily life involves migrating from data centers to AWS, managing hundreds of instances, and tracing the causes of failures in the early hours of the morning.

The reason I created this course is simple.

I often received questions like these from those around me: "I managed to connect to the server, but I don't know what to do next." "There are so many instance types; which one should I choose?" These are the exact same points where I got stuck at first. However, when I looked for resources, I found that most of them were written for people who already knew what they were doing.

So I decided to create the course I wish I had when I first started learning.

My lectures are based on three principles.

First, I help you understand rather than memorize. Instead of just listing commands, I explain why we use them in the first place.

Second, I boldly omit unnecessary content. I do not increase the volume by including information that is useless to beginners right now.

Third, I also talk about what doesn't work. Instead of just listing the pros, I clearly point out cases where it might not be a good fit. For someone who has to make decisions in the field, that is more important.

If you feel a sense of "Ah, I should start from here" after listening to the lecture, then my goal has been achieved.

Please feel free to leave any questions you may have at any time.

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38 lectures ∙ (35min)

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