AI

AI Track | 25 modules | 5 sections | ~43-64 hours

Overview

This is the accessible entry point for learning AI at KubeDojo.

It is for people who need to understand AI well enough to use it safely, productively, and critically before they ever touch model training, MLOps, or AI infrastructure.

This track is not the same as AI/ML Engineering.

AI is the front door for learners and practitioners
AI/ML Engineering is the advanced builder track

Who This Track Is For

learners starting from zero AI background
people who want to use AI well in daily work and study
technical practitioners who want strong AI habits before advanced AI/ML depth
cloud-native learners who need verification, privacy, and trust boundaries

Do Not Start Here If

you already know you want model training, RAG internals, inference infrastructure, or MLOps
your main goal is building production AI systems rather than learning to use AI well

In those cases, start with AI/ML Engineering instead.

Sections

AI Foundations — 6 modules

The conceptual base:

what AI is
what LLMs are
prompting basics
verification
privacy and safety
using AI in real learning and work

AI-Native Work — 4 modules

The practical workflow layer:

tool selection
agents and assistants
workflow design
human-in-the-loop discipline

AI Building — 4 modules

The bridge into system-building:

how AI features differ from chat use
models, APIs, context, and structured output
retrieval, tools, and safe capability boundaries
evaluation, iteration, and shipping a sane v1

Open Models & Local Inference — 7 modules

The practical open-model path:

model hubs and model cards
Hugging Face for learners
quantization and formats
MLX on Apple Silicon
Linux local inference
runtime choice across Ollama, MLX, Transformers, and vLLM
Gemma 4 as a current open-model comparison case

AI for Kubernetes & Platform Work — 4 modules

The practitioner differentiator:

manifest and config review
Kubernetes troubleshooting and triage
platform and SRE workflow support
trust boundaries for infrastructure AI use

Recommended Route

AI Foundations
   |
AI-Native Work
   |
AI Building
   |
Open Models & Local Inference
   |
AI for Kubernetes & Platform Work
   |
AI/ML Engineering (optional advanced path)

What Makes This Track Different

it treats AI literacy as a serious skill, not hype
it emphasizes verification and trust boundaries from the start
it teaches how to use AI in disciplined workflows, not just how to type prompts
it gives a clean bridge into AI/ML Engineering without throwing beginners into deep infrastructure too early

Boundary Rules

The safest way to avoid duplication is to keep the tracks answering different questions.

If the question is…	Stay in…	Do not jump to yet…
what AI is and how to use it safely	`AI Foundations`	deep model or infra phases
how to work with AI without losing judgment	`AI-Native Work`	engineering tooling and framework phases
how to turn AI use into a real product feature	`AI Building`	full MLOps or model-training depth
how open models, runtimes, and local hardware choices work	`Open Models & Local Inference`	production infra unless you truly need it
how AI fits into Kubernetes, YAML, triage, and operator work	`AI for Kubernetes & Platform Work`	platform-scale AI operations unless the work becomes system-building

In other words:

AI = literacy, disciplined use, and practical bridge work
AI/ML Engineering = implementation, deployment, operation, and model/system depth

Good First Clicks

After This Track

Choose the next route based on your goal:

Goal	Next Step
Use AI well in everyday technical work	stay in AI-Native Work and apply it directly
Build real AI systems	go to AI/ML Engineering
Strengthen technical foundations first	go to Prerequisites or Linux

“AI should make your thinking sharper, not replace it.”