When I first encountered the task of setting up monitoring for a microservice architecture, I realized: traditional approaches with Nagios or Zabbix don't work here. I needed a system that allows not just looking at graphs, but understanding why latency spiked, where traces are lost, and how to correlate logs with metrics. That's when I stumbled upon the "Observability (Prometheus, Grafana)" course on the Asibiont platform. Let me tell you why this turned out to be the best solution for my growth as an engineer.
What is Observability and Why It Matters in 2026
Observability is not just a buzzword. It's the ability to ask your system any questions about its state without needing to write new code. It's built on three pillars: metrics (Prometheus), logs (Loki), and tracing (OpenTelemetry). According to the CNCF report for 2025, over 70% of production systems use Prometheus for monitoring, and Grafana has become the standard for visualization. Without understanding SLI (Service Level Indicators) and SLO (Service Level Objectives), it's impossible to build a reliable infrastructure—you simply don't know if the service is performing well enough for the user.
What the Course Actually Teaches
The course is built around practical tasks that every SRE or DevOps engineer faces. The curriculum covers:
- Metrics and Monitoring: Installing and configuring Prometheus, collecting metrics from applications, exporters for databases and web servers.
- Visualization in Grafana: Creating dashboards that show the real picture of system health.
- Tracing with OpenTelemetry: Distributed tracing—how to track a request through dozens of microservices and find bottlenecks.
- Alerting and On-Call: Setting up alerting rules, creating runbooks (instructions for on-call engineers), and conducting postmortems after incidents.
- Blackbox Monitoring: Checking service availability from the outside, monitoring SSL certificates and external endpoints.
I personally set up an alert for p99 latency exceeding 500ms using PromQL queries—and it worked in production the second day after deployment.
How Learning Works on Asibiont
The Asibiont platform uses AI generation for personalized lessons. These aren't typical YouTube videos, but text materials that the neural network adapts to your level. When I started the course, I indicated that I was already familiar with the basics of Linux and Docker, but had never worked with OpenTelemetry. The system automatically generated lessons starting from the basics of distributed tracing, but skipped the introduction to bash, which I didn't need.
Each lesson contains:
- A theoretical block with code examples and configurations.
- A practical task—for example, writing a PromQL query to calculate availability over the last 7 days.
- Self-check questions with explanations from the AI.
The AI tutor doesn't answer in a 24/7 chat, but it generates explanations of complex terms (e.g., what Span and Trace are in OpenTelemetry) in simple language. If I didn't understand the difference between SLI and SLO, I just re-read the generated example with uptime and latency metrics—and everything fell into place.
Why AI Learning is Effective
Traditional courses often suffer from "fluff"—long introductions, outdated examples, and lack of feedback. On Asibiont, the neural network tailors the program to your goals. Want to dive deeper into alerting? The AI adds a module on Alertmanager and integration with PagerDuty. Need more practice with Loki? The system suggests additional log-related tasks.
I appreciated this when, after the third module, I wanted to understand runbooks—the AI generated a lesson with postmortem templates and checklists for on-call engineers. This saved me hours of googling.
Who This Course Is For
The course is designed for engineers who already have basic knowledge of Linux and networks, but want to systematically study observability. If you are:
- A DevOps engineer implementing monitoring in your team.
- An SRE responsible for service reliability.
- A backend developer who wants to understand how your code affects production.
- A system administrator transitioning to cloud infrastructure.
Then this course will give you practical tools for working with Prometheus, Grafana, and OpenTelemetry.
Results After Completion
A month after finishing the course, I:
- Set up monitoring for three microservices on Kubernetes using Prometheus Operator.
- Created a Grafana dashboard with four panels: CPU, memory, request latency, and error rate.
- Wrote a runbook for a "database failure" incident with step-by-step actions.
- Figured out distributed tracing: found a bottleneck in the authorization service where a request hung for 2 seconds.
These skills directly impacted my career—two weeks after publishing the dashboard, I was invited for an interview with an SRE team.
Conclusion
If you want to stop guessing why your application is slow and start seeing the full picture through metrics, traces, and logs, the "Observability (Prometheus, Grafana)" course on Asibiont is what you need. The AI adapts to you, and the curriculum covers all modern observability standards. Start learning today: Observability (Prometheus, Grafana).
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