AI Infra Engineer Learning

Understand the project

Practical overview

Use it when you want a structured, hands-on study path for production ML systems and already meet its technical prerequisites.

Engineers with intermediate Python and foundational ML, Linux, Git, Docker and Kubernetes skills.
Self-directed learners preparing for AI infrastructure engineering work.

Build a structured study path from ML infrastructure foundations to LLM infrastructure.
Learn concepts and workflows for cloud ML, containers, Kubernetes, data pipelines and MLOps.
Work through project material for model serving, an MLOps pipeline and LLM deployment.

Before you choose

The repository identifies the curriculum as free and open source and uses the MIT license.

The learning path is substantial: the README describes 10 modules and more than 500 hours of content.
Its subject range includes cloud platforms, Docker, Kubernetes, MLOps, GPU computing, monitoring, infrastructure as code and LLM infrastructure.
The self-study path expects existing Python, ML, command-line, Git, Docker and Kubernetes knowledge.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a local learning environment and begin Module 01.

Intermediate Python; the setup instructions use Python 3.11.
Linux or Unix command-line skills, Git, ML basics, and introductory Docker and Kubernetes.

A local environment is prepared and the learner has opened the first foundations lesson.

This is a setup and lesson-start guide; it does not cover completing a lesson or running a project.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 11 October 2026
Original website title: ai-infra-curriculum/ai-infra-engineer-learning · Official homepage title · retained in its source language