Didaktik Manufaktur Hamburg
Fundamental AI lab with high-performance workstations for machine learning
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Extension module · CT-WR

Fundamental AI-Extension for Technical and Vocational Schools

Fundamental AI-Extension for technical and vocational schools is not a futuristic robotics center — it is a structured computing environment designed to teach:

  • Programming for AI

  • Machine Learning fundamentals

  • Data handling

  • Model training & evaluation

  • Introductory AI deployment

Key Characteristics

  • 24 high-performance workstations (rows or U-shape)

  • Instructor station at front

  • Large display / projector

  • Dedicated server rack corner

  • Whiteboard for mathematical explanations

  • Strong cooling (AI workloads generate heat)

Hardware Requirements (Fundamental Level)

You do NOT need a supercomputer.

Student Workstations (24 Units)

Minimum:

  • CPU: i7 / Ryzen 7

  • RAM: 32GB (important for ML datasets)

  • Storage: 1TB SSD

  • GPU: NVIDIA RTX 3060 or similar (8–12GB VRAM)

Why GPU?

"Machine learning frameworks like TensorFlow and PyTorch use CUDA acceleration."

If budget is limited:

  • Start without GPUs

  • Use cloud-based AI training (AWS, Azure, Google Colab)

AI Server (Optional but Recommended)

Specification: 128GB RAM, Multiple GPUs (2–4 RTX or A-series), High-speed NVMe storage

10Gb network (optional)

Purpose: Shared model training, Capstone projects, Research simulations

For a vocational school, one strong GPU server is enough.

Software Stack (Core)

Programming & Frameworks

  • Python (primary language)

  • Jupyter Notebook, Anaconda, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy and Matplotlib

Development Tools

  • VS Code

  • Git

  • Docker (for model deployment basics)

Operating Systems

  • Ubuntu Linux (preferred for AI)

  • Windows (optional dual boot)

Curriculum Structure for a Fundamental AI Lab

Focus: Hands-on labs > theory.

Level 1: Foundations

  • Python programming

  • Linear algebra basics

  • Statistics for AI

  • Data cleaning

  • Visualization

Level 2: Core Machine Learning

  • Supervised learning

  • Regression

  • Classification

  • Model evaluation

  • Overfitting & bias

Level 3: Intro to Deep Learning

  • Neural networks

  • Image recognition

  • Basic NLP

  • Simple AI deployment

What Makes It "Fundamental" ?

A fundamental AI lab is:

  • Educational, not research-grade

  • Focused on applied ML

  • Designed for skill development

  • Built around Python ecosystem

  • Integrated with industry certification paths

It is not: A robotics lab, a humanoid lab, a robotics factory or a data center !

Upgrade to an AI Fundamental option

Contact us for a detailed quotation if you want to upgrade or convert your Computer / IT Department with an AI Fundamental option.

Your DMH-Team!

Contact Sales