What is this course
A PRACTICAL UPGRADE
You already know DevOps.
Now learn how AI changes the way you work.
Now learn how AI changes the way you work.
01
02
03
Understand AI deeply
APPLY AI TO REAL WORK
Build WORKFLOWS
Understand how LLMs, context, models, agents, and MCP work so you know where AI is reliable, where it fails, and what should never be delegated to it.
Use AI with Terraform, Kubernetes, CI/CD, logs, alerts, monitoring systems, runbooks, GitHub/GitLab, and real operational data.
This is the central promise of the course: the relevant skill is increasingly not simply prompting an LLM, but designing safe, controlled and traceable AI workflows around production systems.
This is not a course about learning DevOps from scratch.
And it is not about MLOps or deploying machine-learning models.
It is about using AI for DevOps work — infrastructure, CI/CD, incident response, monitoring, remediation, documentation, security, and operational workflows.
And it is not about MLOps or deploying machine-learning models.
It is about using AI for DevOps work — infrastructure, CI/CD, incident response, monitoring, remediation, documentation, security, and operational workflows.
Who already work with infrastructure and CI/CD and want to integrate AI into their everyday engineering workflows.
DevOps Engineers
Cloud Engineers
Who regularly work with infrastructure-as-code, Kubernetes, deployment systems and operational automation.
Site Reliability Engineers
Who want to use AI to correlate alerts, logs, metrics and deployments and investigate incidents faster.
Infrastructure Engineers
Who want AI to help review, diagnose, document and safely modify infrastructure.
Platform Engineers
Who want to build internal AI-enabled workflows and tools for engineering teams.
DevOps / SRE Team Leads
Who need to understand where AI automation is genuinely useful, where it introduces risk, and how to introduce AI safely inside their teams.
Who is this for
BUILT FOR ENGINEERS WHO WANT TO WORK AI-NATIVE
AIOps: From Alerts to Root Cause
05
Build Your Own MCP Tool
04
Safety, Security & Production Guardrails
Capstone: Presentation & Wrap-up
07
08
From Diagnosis to Action
06
AI for CI/CD, Code Review & Bug Fixing
03
AI CLI, Infrastructure Code & MCP
02
LLMs, Context & Prompting for Engineers
01
Curriculum
EIGHT SKILLS.
ONE MONTH.
ONE MONTH.
Send your application. We will follow up with a short questionnaire and the technical requirements before payment.
Secure your spot now
Start your journey at ACA X
Course Fee: 100,000 AMD
Get up to 100,000 AMD back if you submit your annual tax declaration and have income tax.
Master AI skills.
Build your future.
Lead the change.
Build your future.
Lead the change.
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About ACA
ACA X IS BACKED
BY 10 YEARS OF
TECH EDUCATION
BY 10 YEARS OF
TECH EDUCATION
ACAx is the professional AI direction of Armenian Code Academy - one of Armenia's most established tech education institutions, founded in 2015.
For a decade, ACA has trained engineers, designers, product managers, and data professionals who now work at global companies, local unicorns, and fast-growing startups. ACAx is the next evolution -professional AI skills for everyone, not just engineers.
For a decade, ACA has trained engineers, designers, product managers, and data professionals who now work at global companies, local unicorns, and fast-growing startups. ACAx is the next evolution -professional AI skills for everyone, not just engineers.
10+
YEARS IN TECH EDUCATION
17K+
TOTAL GRADUATES
PARTNER COMPANIES
100+
300+
TUTORS
FREQUENTLY ASKED QUESTIONS
The course is designed primarily for DevOps Engineers, SREs, Platform Engineers, Cloud Engineers and Infrastructure Engineers who already work with Kubernetes, infrastructure as code, and a CI system.
Basic familiarity with AI tools is helpful. This is not a beginner prompting course — it is an advanced practical program for technical professionals who want to apply AI across engineering workflows.
No. Coding is only part of it. The course covers infrastructure code (Terraform, Kubernetes), CI/CD and automated bug fixing, connecting AI to your real systems through MCP, incident diagnosis and alert noise, agents that take action with human approval, and the safety rules that make all of this usable in production.