OS & Linux Fundamentals

Learn how operating systems work at the process and file level. Master the terminal, log management, and resource monitoring — the foundation every AIOps engineer builds on.

2Modules

3Resources

02

Step 02Step 02

Networking & Cloud Basics

Understand how data moves across networks and cloud infrastructure. Learn to provision and observe cloud components so you can deploy and inspect AIOps workloads.

3Modules

0Resources

03

Step 03Step 03

Python for Ops

Write Python scripts to process, automate, and analyze operational data. Learn Pandas for log analysis and API integration for the pipelines that power every AIOps system.

1Modules

0Resources

04

Step 04Step 04

DevOps & CI/CD Foundations

Master containers, version control, and deployment pipelines. Learn to containerize AIOps workloads, manage Kubernetes events, and run incident postmortems

4Modules

2Resources

05

Step 05Step 05

Monitoring & Observability

Collect and visualize metrics, logs, and traces. Master Prometheus, ELK, and OpenTelemetry so you can instrument any system for AI-driven analysis.

2Modules

0Resources

06

Step 06Step 06

ML & Anomaly Detection

Apply machine learning to detect patterns and predict failures in operational data. Learn the three techniques used in real production AIOps systems.

2Modules

0Resources

07

Step 07Step 07

Distributed Systems & Reliability Engineering

Understand why distributed systems fail and how to design for resilience. Learn the mental models that explain why incidents happen at scale.

1Modules

0Resources

08

Step 08Step 08

Data Pipelines & Streaming

Build pipelines that ingest operational data in real time. Learn Kafka for high-throughput event processing and InfluxDB for time-series storage.

1Modules

1Resources

09

Step 09Step 09

Prompt Engineering for AIOps

Write prompts that make AI reason correctly about ops problems. Learn structured output, chain of thought, and RCA prompt templates for real incident workflows.

2Modules

0Resources

10

Step 10Step 10

Model Ecosystem & Local Inference

Understand the full landscape of AI models — cloud and open source — and learn to run them locally for sensitive ops workloads where log data cannot leave your infrastructure. Prerequisite: completed Prompt Engineering section.

1Modules

0Resources

11

Step 11Step 11

AI Agents & MCP

Build AI systems that take actions, call tools, and operate autonomously inside ops environments. Learn to give agents safe, structured access to your production systems.

2Modules

0Resources

12

Step 12Step 12

Agent Orchestration & RAG

Connect multiple agents together and ground them in your internal knowledge. Build workflows that triage alerts, retrieve runbooks, and coordinate across complex incidents.

1Modules

0Resources

13

Step 13Step 13

Controls, Guardrails & Security

Build AIOps systems that are safe, compliant, and auditable in production. Prevent AI from executing dangerous commands and integrate safely into DevSecOps pipelines.

1Modules

0Resources

14

Step 14Step 14

Core AIOps Concepts & Platforms

Connect monitoring, ML, and agents into real AIOps workflows. Master event correlation, root cause analysis, alert noise reduction, and the platforms that power AIOps at scale.

2Modules

0Resources

15

Step 15Step 15

Auto-Remediation & Self-Healing

Build systems that detect and resolve issues automatically without human intervention. Implement Ansible-powered runbooks triggered by alerts to reduce MTTR from hours to seconds.

1Modules

0Resources

16

Step 16Step 16

Real-World Projects & Capstone

Synthesize everything into production-grade AIOps systems. Build five portfolio projects that demonstrate end-to-end AIOps engineering capability — built as if a hiring manager will run them.

0Modules

0Resources

AIOps Engineering Mastery

How to Use This Roadmap

Curriculum Breakdown

OS & Linux Fundamentals

Networking & Cloud Basics

Python for Ops

DevOps & CI/CD Foundations

Monitoring & Observability

ML & Anomaly Detection

Distributed Systems & Reliability Engineering

Data Pipelines & Streaming

Prompt Engineering for AIOps

Model Ecosystem & Local Inference

AI Agents & MCP

Agent Orchestration & RAG

Controls, Guardrails & Security

Core AIOps Concepts & Platforms

Auto-Remediation & Self-Healing

Real-World Projects & Capstone

Join the Network

Salary Outcomes

Explore Other Roadmaps

DevSecOps Mastery Path

🗺️DevOps Roadmap