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AI-Powered Observability for Modern DevOps

Course Details

Duration

1.5 Months

Date

Time

Lectures

0 Learning Mode

Online

Certificate

Included

Total Enrolled

0

Course Overview

AI-Powered Observability for Modern DevOps is a hands-on technical workshop for DevOps engineers, SREs, and developers who want to move beyond basic monitoring into full-stack observability. You’ll explore the 3 pillars of observability — Metrics, Logs, and Traces — using industry-standard tools like Prometheus, Grafana, Jaeger, and OpenTelemetry. The course also introduces AIOps, showing how AI/ML is transforming incident detection, alerting, and root cause analysis in modern distributed systems.

Key Takeaways

Understand the difference between monitoring and observability in microservices environments
Master the 3 pillars — Metrics, Logs & Traces — and how they work together
Instrument, visualize, and trace live applications using OpenTelemetry, Prometheus, Grafana & Jaeger
Debug real incident scenarios (latency spikes, service failures) using an SRE mindset
Explore AIOps — anomaly detection, intelligent alerting & root cause suggestions
Learn observability best practices for Kubernetes and cost-efficient architectures

Prerequisites

Basic understanding of DevOps concepts and microservices architecture
Familiarity with any one programming language (Python or Node.js preferred)
Exposure to Linux command line
Laptop with Docker installed (for hands-on demo stack)

Course Curriculum

Level 1 — Observability Fundamentals

Monitoring vs Observability — why the difference matters
Microservices complexity & the role of SRE
Golden Signals, SLI, SLO & SLA explained

Level 2 — Core Pillars & Tooling

The 3 Pillars — Metrics, Logs & Traces
Tools overview — Prometheus, Grafana, Jaeger & OpenTelemetry
How correlating all 3 pillars gives real system visibility

Level 3 — Hands-on Observability Stack

Instrument an app using OpenTelemetry
Capture & visualize metrics with Prometheus & Grafana
Trace requests end-to-end using Jaeger
Generate live traffic & observe latency spikes in real time

Level 4 — Incident Debugging (SRE in Action)

Scenario-based debugging — API latency spikes & service failures
Debug flow: Metrics → Logs → Traces → Root Cause
Reducing MTTR & improving RCA quality

Level 5 — AI-Powered Observability (AIOps)

What is AIOps & how it applies to observability data
From reactive → predictive → autonomous operations
AI use cases — anomaly detection, log clustering & intelligent alerting
Tools: Datadog, Dynatrace (Davis AI) & New Relic

Level 6 — Architecture & Best Practices

Instrumentation strategy & sampling in tracing
Cost vs visibility trade-offs
Observability in Kubernetes
Mini Challenge — diagnose a real dashboard/log snippet

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