Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We run this course. Content, prerequisites and dates come from its program page, checked on October 28, 2026.
DevOps engineers are used to evaluating tools by whether they remove toil without adding risk. That is a good test for this course as well. This post goes through what it automates, what stays manual, and what you need before you start.
What the four modules cover
| Module | Topic | What you do |
|---|---|---|
| 1 | Generative AI for infrastructure automation | Generate and review infrastructure code, pipelines and configuration with live coding |
| 2 | Agentic AI for incident and cost automation | Build agents for drift detection, incident response and cost review |
| 3 | Security and monitoring integration | Connect the agents to security checks, compliance checks and monitoring |
| 4 | Build and deploy your project | Build and deploy a project using what the first three modules taught |
12 hours, 4 modules
Additional discounts are available. Contact us to know more.
What AI should and should not touch in infrastructure
The risk with AI in operations is not that it writes bad code. It is that it writes plausible code that you apply without reading it. The course frames generated infrastructure as a draft that goes through your normal review, and the agents it builds are ones you can scope: read-only checks first, with write actions behind approval. That is a sensible default for any team, whether or not it takes the course.
A test of fit
| If this describes you | Verdict |
|---|---|
| You manage pipelines, containers and cloud accounts and want to automate review work | Good fit |
| You spend time on incident triage, drift checks or cost reviews | Good fit. Module 2 targets those tasks |
| You want to prove AI-assisted changes are safe before you adopt them | Good fit. Module 3 covers security and monitoring |
| You have never used Git, CI/CD or Docker | Not a fit yet. The course assumes them |
| You want to build machine learning platforms | Look at the MLOps, LLMOps and Agentic AI Ops BootCamp instead |
Cost, time and prerequisites
| Item | Detail |
|---|---|
| Fee (US) | $450, with a monthly payment option from about $19 |
| Fee (India) | ₹25,000 for the cohort listed on the page, with 7 PM to 11 PM IST sessions over three days starting November 9, 2026 |
| Hours | 12 live hours |
| Prerequisites | Python for AI (6 hours), familiarity with Git, CI/CD and Docker, a cloud account, and comfort in a terminal |
| Extra cost | About $10 of API credit for the labs, according to the page |
| Assessment | No formal exam. A certificate of completion is awarded |
What a course will not give you
- A policy for what AI may change in your production environment. That decision is yours and your security team’s.
- A replacement for your review process. Generated changes should go through it.
- Knowledge of your company’s tooling. The labs use general examples, so applying them to your stack takes your own work.
For more detail, read the skills post, the complete guide and the full cost breakdown. For the career view, see DevOps Engineer to AI Infrastructure Engineer. The program page is GenAI & Agentic AI for DevOps Engineers.
Questions people ask
Do I need a cloud account?
Yes. The page lists a cloud account as a prerequisite, because the agents read cloud state.
Is there an exam?
No formal exam. You receive a certificate of completion.
Are sessions recorded?
Yes. Live sessions are recorded.
