A platform is not a model

How this Build Your Own AI Platform track works

Read and then write — each lesson asks for a check you can show: an input, a pass rule, and a failure case.
Python is local — sketches run on your machine. This track does not pretend the browser trained a model.
Prerequisites — comfort with Python and the Data Science habit of holding out data you do not train on.
Pair with — AI for vocabulary and Generative AI when the system calls a language model.

The product around models: registry, training jobs, inference, data, auth, cost, and a way to turn a bad model off.

Install on your device (macOS, Linux, Windows)

Install Python 3.11+ locally for notebooks and frameworks; the on-site playground uses the dev runner when enabled.

macOS

brew install python@3.12 or install from python.org (check “Add to PATH” on installers).
Create a project folder: mkdir ~/python-practice && cd ~/python-practice.
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip

Linux

Debian/Ubuntu: sudo apt update && sudo apt install -y python3 python3-pip python3-venv
Fedora: sudo dnf install -y python3 python3-pip
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip

Windows

Install from python.org and enable Add python.exe to PATH.
Or: winget install Python.Python.3.12
PowerShell: py -3 -m venv .venv; .\.venv\Scripts\Activate.ps1
pip install --upgrade pip

Verify: python3 --version (or py --version on Windows) shows 3.11+.

Run code on this site (Backend & language playgrounds)

Clone or open this project locally; copy .env.example to .env.
Ensure LEARNING_RUNNER_ENABLED=true and LEARNING_RUNNER_URL=http://127.0.0.1:9999/v1/execute.
Terminal 1: php artisan serve (or composer run dev for Laravel + Vite + runner together).
Terminal 2: npm run runner — keep it running while you click Run on server.

A platform is not a model on the Build Your Own AI Platform track. An AI platform is the system that stores data and models, runs training and inference, checks permission, and records what happened. The model is one artifact inside it.

This is the first lesson. Read it before you change any parameters or call any tool.

The idea in practice

List the jobs the platform must do even if the model is a simple baseline. If the list is only 'call an API', you do not need a platform yet.

A concrete check

goal = {
    'track': 'Build Your Own AI Platform',
    'lesson': 'A platform is not a model',
}
checks = [
    'input available at decision time',
    'score matches the real decision',
    'failure case written down',
]
print(goal['lesson'])
for item in checks:
    print('-', item)

Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.

What usually goes wrong

Building a platform before one model has a user. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.

What to write down

The input you are allowed to use at decision time.
The output and the score or pass rule.
One failure you will test on purpose.
What you will not claim the system can do.

Practice

Write the smallest platform you need for one internal model.

Self-check

Say A platform is not a model in one sentence that mentions an input and an output.
Name the failure mode in this lesson and the check that would catch it.

Done when: you can explain this lesson without the page open, and you have a written failure case.

This lesson