Your ML model isn't the whole AI system anymore

For a long time, when we talked about an ML project, the model was usually at the center of everything.

Get the data.
Train the model.
Improve the accuracy.
Deploy it.
Monitor it.

Pretty straightforward.

But AI applications are starting to feel different.

Imagine you build a system that answers questions about your company's internal data.

Having a good model is important, but that's only the beginning.

Where does the information come from?

What if the answer isn't in one document?

What if the system needs to search, think through a few steps, use a tool, and then come back with an answer?

What if a smaller model is better for one part of the job and a larger one is better for another?

And probably the hardest question:

How do you know the answer is actually good?

Suddenly, the model isn't doing everything.

There's a whole system around it: information, different models, actions, checks, and people when needed.

I think this is one of the biggest changes in how we should think about building AI applications.

We're not just building a model anymore.

We're building a system that knows how to use intelligence.