Course: AI Reliability Engineering 2.0

Overview

This course titled Course: AI Reliability Engineering 2.0 offers an immersive, hands-on path from Kubernetes controllers to advanced AI reliability practices. Framed as the third era of DevOps and SRE, the program blends modern DevOps concepts with practical AI infrastructure management to help participants reliably build, scale, and automate AI systems. The training is delivered online via Zoom, with all lectures, materials, and recordings accessible on the learning platform. Language for participants is Ukrainian, with presentation materials in Ukrainian and English, and the program emphasizes practical work over theory.

What it is for

This program is designed for developers, QA engineers, system administrators, and IT professionals who want to apply Kubernetes and SRE principles to AI workloads. It is especially suited for those who aim to leverage GitOps style patterns, agentic AI, and AI observability within Kubernetes clusters. Attendees should expect to engage in collaborative work, hardware and software exercises, and real-world scenarios that closely resemble production environments.

Format and schedule

Duration is three weeks of online classes held on Mondays, Wednesdays, and Fridays. Classes start on May 4th and run through May 22nd, 2026. The platform is Zoom, with broadcasts, lecture recordings, and course materials available on the learning platform. Participants will work in teams and pairs to mirror real project collaboration. The event is described as online and offline in a project-like format, with practical tasks and infrastructure challenges that require configuration, automation, and problem solving in a production-like setting. The course language is Ukrainian for the event, with presentation language in Ukrainian and English.

Curriculum and agenda

The course covers a comprehensive stack of topics including practical Kubernetes, GitOps vs Gitless Ops, and Agentic AI patterns. A notable module is Building a Real Agentic AI Project from Scratch, which outlines the SDLC, infrastructure, network, security, and frameworks involved in deploying agents and observing their behavior. The Stack section outlines MCP/Agents Serving Challenges, AI SDLC flows using tools such as kmcp and Arize Phoenix, and security and compliance practices for prompts and MCP authentication. The Plan module introduces hands-on activities like configuring containers and Kubernetes clusters, Git/GitLess Ops practices, and building AI agents within Kubernetes using MCP/A2A. The curriculum emphasizes AI observability, monitoring, and evaluation through observability tools and real-time tracing.

Outcomes and speakers

Mentor Denys Vasyliev, a Principal Site Reliability Engineer with 17+ years in the industry and a Kubernetes Certified Administrator, leads the program. He is an active speaker on major DevOps and SRE platforms and is the author of Kubernetes DIY courses and related content. Course outcomes include practical Kubernetes skills, a deep understanding of modern DevOps/SRE processes, and experience implementing AI agents in a cluster. The program aims to equip attendees with hands-on capabilities to design, deploy, observe, and maintain AI-enhanced systems in production settings.

FAQ and additional information

The event explicitly notes that tickets are sold out while inviting interested participants to join a waiting list for future iterations. It highlights the requirement for basic Linux knowledge and programming basics to perform practical tasks. Group discounts are available for companies, and there is guidance to contact the academy team for calculating discounts. Although the page emphasizes online delivery, it presents a realistic, project-oriented experience intended to resemble real-world production work.

Who should attend

Developers, QA engineers, system administrators, DevOps/SRE practitioners, and anyone looking to leverage Kubernetes for AI projects and to adopt reliable AI infrastructure practices. The course is designed for those who want to integrate AI agents in a Kubernetes environment and gain hands-on experience with modern AI reliability workflows.