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AI-Powered Observability for Modern DevOps

Course Details

Duration

10 Hours

Date

Sep 20, 2026

Time

10:00 am

Lectures

0

Learning Mode

Online

Certificate

Included

Total Enrolled

0

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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

  1. Basic understanding of DevOps concepts and microservices architecture
  2. Familiarity with any one programming language (Python or Node.js preferred)
  3. Exposure to Linux command line
  4. 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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