AIOps Engineer

AIOps Engineer Learning Path

A structured route into AI-driven operations: build a Linux foundation with RHCSA, master containers with Docker and Kubernetes (CKA), then move into Generative AI and running AI/ML workloads on Kubernetes with Kubeflow.

4 Steps 4 Courses
curriculum

Path Curriculum

1

Linux & Automation Foundation

Build a strong Linux foundation with RHCSA and automation with RHCE.

RHCSA Training on RHEL-10 2 Months · Beginner RHCE Training on RHEL-10 2 Months · Advanced
2

Containers & Orchestration

Master Docker and Kubernetes (CKA) for containerized workloads.

Docker Containers 1.5 Months · Intermediate Kubernetes CKA 2 Months · Advanced
3

Generative AI & AI/ML

Learn Generative AI concepts and AI/ML workloads on Kubernetes.

4

Kubeflow & MLOps

Deploy and manage AI/ML pipelines with Kubeflow on Kubernetes.

Ready to Start Your Journey?

Follow this structured path to become a AIOps Engineer.

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