Train Your First AI Model With Zero Code Today

Train Your First AI Model Today.

Today you’re going to train your first AI model. Absolutely no code and no technical ability required. You just need to have a computer and the ability to type English words. That’s it.

First, we are going to cover why being able to fine-tune models is one of the most desirable enterprise skills of the next ten years. And no, not just for AI engineers, as the technical barrier is so low that anyone can train them.

You could be an art major with exactly 0 coding skills, and you can still train models these days.

And I’m about to prove it to you today.

To be a good model trainer, you just need to learn how to think in first principles about the results you’re seeing. It’s not about having Math Olympiad medals, 4.0 GPAs, or a 160 IQ. Instead, it’s mostly intuition driven by experience.

Through a series of small training runs toward a desired model behavior, we’re going to see the progression of the experimentation required, with all the in-between lessons (balancing, preservation, set sizing, multi-turn preservation, hyperparameter tuning, etc.), and other tricks researchers do to churn out models, ending up with your first fine-tuned AI.

Next stop, training yours. Let’s dive in.

Your Sovereign Stack

As this piece is entirely about teaching you a simple way to train your own models on any task or dataset you want, we need to understand why it is such an important skill to learn.

The importance of data

If you train an AI to predict the price of used cars by getting information about their mileage, age, model version, prices of sold similar cars, etc., the model will quickly learn to associate which characteristics correlate with higher prices and which ones put downward pressure on prices.

Newer cars tend to be more expensive; more mileage tends to reduce the price; accidents reduce the price; more powerful engines often increase the price; luxury brands tend to command higher prices; certain models depreciate faster than others.

Imagine we give the model thousands of examples like “2022 BMW, 20,000 miles, no accidents → $35,000” and “2015 BMW, 120,000 miles, two accidents → $9,000.”

Then the model starts discovering patterns such as “as mileage increases, price tends to decrease” or “holding everything else constant, newer cars tend to cost more.”

By seeing thousands or even millions of examples, models will eventually pick up more subtle patterns, such as “100,000 miles might hurt the value of a Ferrari much more in dollar terms than a Toyota” and eventually develop intuitions that can only be built after seeing millions of examples, something no human can do.

The point here being: data is at the center of it all. Data creates the models; data decides whether the AI learns the fact or skill.

If we accept this to be true (it’s undeniable), then you quickly understand how important it is that, if you want a model to be good at…

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