How to Stop Fighting Fires in Production: The Observability Course (Prometheus, Grafana) from ASI Biont

Introduction: Why Observability Is Not Just "Monitoring" but a Lifesaver for Engineers

Have you ever woken up at 3 AM because production went down, but your monitoring was silent? Or spent hours troubleshooting an incident because logs were scattered across three different systems and metrics didn't align? If so, you're not alone. According to Datadog's 2025 report, over 60% of engineers admit they spend twice as much time debugging production incidents as necessary due to the lack of a unified observability system.

Chaotic monitoring is the scourge of modern IT teams. You set up a couple of dashboards in Grafana, add a CPU alert, and think everything is under control? The reality is: without SLI/SLO, tracing, and well-thought-out runbooks, you risk missing critical failures or drowning in false positives.

That's where the Observability (Prometheus, Grafana) course on the ASI Biont platform comes in. This is not just another collection of video tutorials like "Set up Prometheus in 10 minutes." It's a systematic approach to building a production-ready observability system—from metric collection to on-call and postmortem. Let's break down what you'll learn and why AI-powered learning on ASI Biont is the modern standard.

What Is Observability and Why You Can't Do Without It in 2026

Observability is a property of a system that allows you to understand its internal state from external data: metrics, logs, and traces. Unlike classic monitoring, which answers "What broke?", observability answers "Why did it happen?"

The key difference lies in the approaches:

The course on ASI Biont teaches the second approach. You won't just learn how to install Prometheus and draw graphs. You'll master the full cycle: from collecting metrics with OpenTelemetry to building SLO dashboards and writing runbooks that actually help during on-call.

What You'll Learn: Practical Skills

The course curriculum is designed to give you concrete, battle-tested skills. Here are the key modules:

1. SLI/SLO — Metrics That Matter

Most engineers set up monitoring chaotically: "Let's add an alert for 90% RAM." But observability starts with defining what truly matters for your system. SLI (Service Level Indicator) is a measurable quality metric, like API response time or error rate. SLO (Service Level Objective) is the target value of the SLI that you promise to users.

In the course, you'll learn:
- How to formulate SLIs for different service types (web apps, databases, queues)
- How to calculate SLOs based on historical data
- How to build burn rate alerts that warn you about SLO violation risks hours in advance, not days

Practical example: Suppose your REST API must respond within 200 ms in 99.9% of cases over a month. Without SLO, you'll find out about a problem when users start complaining. With SLO alerting, you'll get a notification when the burn rate (the speed at which you're "burning" through your SLO) exceeds the norm—and you'll have time to fix it before customers are affected.

2. Prometheus and OpenTelemetry: Collecting Metrics and Traces

Prometheus is the de facto standard for metric collection in Kubernetes and microservice architectures. OpenTelemetry is a universal framework for collecting traces and metrics, replacing proprietary solutions.

In the course, you will:
- Set up a Prometheus server to collect metrics from applications and infrastructure
- Learn to write exporters and use ready-made ones (Node Exporter, Blackbox Exporter)
- Master distributed tracing with OpenTelemetry: how to trace a request across microservices
- Set up Loki for centralized log collection with the LogQL query language

Case study: Imagine a user complains about slow page loading. Without tracing, you'll have to guess which of 15 microservices is slow. With distributed tracing, you see: the request passes through Service A (10 ms), Service B (200 ms!), Service C (5 ms). The problem is localized in a minute.

3. Alerting and Runbooks: How Not to Drown in Night Calls

Alerts are a double-edged sword. Too many, and the team stops responding. Too few, and you miss critical failures. In the course, you'll learn to configure Alertmanager in Prometheus so that alerts are meaningful.

What you'll learn:
- Alert grouping rules (to avoid receiving 100 emails during a single failure)
- Inhibition and silences (to avoid disturbing the team during planned maintenance)
- Writing runbooks—instructions that help the on-call engineer quickly restore the service

Real example: At Company N., after implementing runbooks, the mean time to recovery (MTTR) dropped from 45 minutes to 8 minutes. The runbook included clear steps: check logs in Loki, run a curl to the endpoint, restart the pod. Without it, the engineer wasted time searching for documentation.

4. Blackbox Monitoring: Monitoring What You Don't Control

Not everything can be covered by agents. Blackbox monitoring checks the system from the outside: via HTTP, TCP, ICMP. In the course, you'll set up the Blackbox Exporter to monitor SSL certificates, external APIs, and DNS.

Practical scenario: Your website is down, but Prometheus inside the cluster shows 100% uptime. Why? Because the problem is at the CDN or DNS level. Blackbox monitoring from the outside will show the real picture.

5. On-Call and Postmortem: A Blameless Culture

Observability isn't just about tools—it's also about processes. You'll learn:
- How to organize on-call rotations without burnout
- How to conduct postmortems (incident reviews) without blaming anyone
- How to use observability data to improve SLA

Who Is This Course For?

The Observability (Prometheus, Grafana) course on ASI Biont is designed for engineers who already work with production systems and want to take their monitoring to the next level. Here are the specific roles:

• DevOps/SRE Engineers — you're responsible for infrastructure stability and want to implement SLI/SLO
• Backend Developers — you write microservices and want to add observability (OpenTelemetry)
• Team Leads / Tech Leads — you're responsible for service quality and want to build an on-call culture
• Linux Administrators — you manage servers and want to move from Nagios/Zabbix to a modern stack

It's not suitable if you're just starting to learn Linux and don't know what Docker is. The course assumes basic knowledge: command-line skills, understanding of network protocols, experience with containers.

How Learning Works on ASI Biont: AI Personalization

Now for the interesting part—how you'll actually learn. The ASI Biont platform uses a neural network to generate personalized lessons. These aren't pre-recorded videos—it's an interactive text-based course that adapts to you.

Why AI Learning Is Effective

1. Adaptation to your level. If you're already familiar with Prometheus, the neural network will skip basic topics and go straight to SLO. If you're a beginner, it will start by explaining what metrics are.
2. Explaining complex concepts in simple terms. The neural network can find analogies. For example, distributed tracing is explained as "tracking a restaurant order's path."
3. Practical tasks with feedback. You don't just read theory—you complete assignments like "Set up Blackbox Exporter to monitor google.com," and the AI checks your answer.
4. 24/7 access. Unlike webinars with fixed schedules, you learn at your own pace. You can pause on a difficult topic, come back the next day, or complete a module in one evening.
5. Answers to questions. The AI tutor generates answers to your questions within the lesson—no need to wait for a mentor.

Important: The AI doesn't provide 24/7 real-time chat support, but it's embedded in every lesson and can explain unclear points or provide additional information on request.

What Learning Looks Like:

• You log into the platform and select the course
• The neural network generates the first lesson based on your level (you can take a test to determine it)
• Each lesson includes theory with code examples (e.g., Prometheus configuration for metric collection)
• After theory, there's a practical task: "Write a PromQL query to calculate the 95th percentile of response time"
• The AI checks your solution and provides explanations
• At the end of the module, there's a test for reinforcement

Example: How AI Helps You Understand PromQL

Suppose you don't understand how the histogram_quantile function works. The neural network won't just give a dry definition: "A function for calculating quantiles based on a histogram." It will offer an analogy: "Imagine you're measuring your server's response time. You record how many requests completed within 100 ms, how many within 200 ms, and so on. histogram_quantile takes this data and says: 95% of requests complete faster than 150 ms."

Then the AI will show a real example of setting up a histogram in a Go application with OpenTelemetry and a Prometheus query:

histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

And explain each part of the query.

Why Choose ASI Biont?

There are many courses on Prometheus and Grafana on the market. But most are static videos recorded a year ago. Observability is a fast-changing field: new versions of OpenTelemetry appear, best practices evolve. ASI Biont solves this problem:
- AI generates up-to-date lessons based on the latest tool versions. You don't need to wait for course updates—the neural network adapts the material to the current reality.
- Personalization. You don't waste time on what you already know. Some need a basic explanation of Prometheus, while others want to dive deep into distributed tracing. The AI adapts.
- Text format. Research shows that reading from a screen allows faster absorption of technical material than watching videos (you can re-read a complex paragraph, go back to a code example).

Conclusion: From Chaos to Stability

If you're tired of nighttime incidents, scattered logs, and alerts that no one reads—the Observability (Prometheus, Grafana) course on ASI Biont is your chance to systematize your knowledge. You'll not only learn the tools but also understand how to build a production observability system from scratch: from metric collection to on-call and postmortem.

Start learning right now—the AI tutor will tailor the program to your level, and within a few lessons you'll be able to:
- Set up Prometheus and Grafana for microservice monitoring
- Formulate SLI/SLO and configure burn rate alerts
- Write runbooks that actually help during on-call
- Use OpenTelemetry for distributed tracing

Don't put it off until tomorrow—production won't wait. Go to the course page: Observability (Prometheus, Grafana) and take the first step toward stable service operation.