Azure AI에서 AIOps까지—DevOps를 먼저 알지 못한 채

Source: Dev.to

What is AIOps, really?

AIOps = Artificial Intelligence for IT Operations

It means using AI on logs, metrics, alerts, and system data to:

• Detect problems early
• Find patterns humans usually miss
• Explain incidents faster
• Reduce manual operational work

The goal is not to replace engineers, but to make operations smarter.

Do you really need DevOps first?

The biggest misconception: “I must fully learn DevOps before touching AIOps.”

In reality, AIOps is more about data understanding and problem‑thinking than about tools. If you already:

• Understand basic cloud concepts
• Have worked with Azure services
• Have some exposure to AI or ML

then you’re already halfway into the AIOps mindset. DevOps tools help later—they are not the starting point.

Why Azure AI works so well for AIOps

Azure’s AI services are built for real operational use, not just experiments. Key services that fit naturally into AIOps include:

• Azure Monitor & Log Analytics – the hub for operational data (VM logs, application logs, metrics, alerts). AIOps starts with this data.
• Azure Machine Learning – built‑in models for anomaly detection, trend analysis, and forecasting; no deep ML expertise required.
• Azure Cognitive Services – Text Analytics and anomaly detection are extremely useful for log and error analysis.
• Azure OpenAI – enables log summarization, incident explanation, and root‑cause suggestions, turning thousands of log lines into clear, human‑readable insights.

A practical path to AIOps (without DevOps pressure)

Step 1 – Learn the basics

• What is a log?
• What is a metric?
• Why do alerts happen?

Spin up a simple VM or App Service and explore Azure Monitor. That’s enough to start.

Step 2 – Use AI to detect problems

Think first, tool second. Example questions:

• Is a CPU spike normal or an anomaly?
• Do repeated errors indicate a hidden pattern?

Azure ML or Cognitive Services can help answer these questions.

Step 3 – Add explainability

Azure OpenAI shines here. Instead of a human spending 30 minutes reading thousands of log lines, AI can provide a summary in seconds, e.g.:

“The incident was likely caused by a memory leak combined with a traffic spike.”

That explanation is AIOps in action.

Step 4 – Touch DevOps gradually

DevOps can’t be skipped forever, but once you know:

• Which problems repeat
• Which fixes can be automated

learning scripts, pipelines, or other automation becomes much easier and more meaningful.

Is AIOps a shortcut to a job?

No. It does, however, turn you into:

• A better problem solver
• An intelligent operations engineer
• A future‑ready cloud professional

Companies now want people who understand systems and think deeply, not just tool operators.

Final thoughts

If you think, “I don’t know DevOps, so AIOps is not for me,” change that mindset today. Start with Azure AI, analyze small problems, and let AI explain what’s happening. AIOps isn’t learned in a day—it’s built over time. Curiosity matters more than tools.

If you work with Azure, cloud, or AI—AIOps is already closer than you think.