Prerequisites

AI/ML Engineering Track | Phase 0

Best for: learners who need a clean local-first starting point before serious AI/ML work.

This phase gives you the foundation that prevents the rest of the track from turning into environment drift, notebook chaos, or unrealistic hardware assumptions.

What This Phase Solves

• choosing a realistic workstation or laptop setup
• keeping Python, CUDA, and ROCm environments reproducible
• learning when notebooks help and when they become a liability
• building habits that survive the jump from experiments to real projects

Modules

#	Module
1.1	Prerequisites & Environment Setup
1.2	Home AI Workstation Fundamentals
1.3	Reproducible Python, CUDA, and ROCm Environments
1.4	Notebooks, Scripts, and Project Layouts

Recommended Order

Read this phase in sequence.

Why:

• 1.1 gets the base environment under control
• 1.2 sets realistic hardware expectations
• 1.3 prevents dependency and driver drift
• 1.4 gives you a project structure that will still make sense later in MLOps and LLMOps work

Where To Go Next

• go to AI-Native Development if your next goal is coding agents, prompt workflows, and AI-assisted engineering
• go to Generative AI if you already have the environment stable and want model fundamentals
• go to AI Infrastructure early if you specifically want a local inference and home-lab path