Fine-Tuning Phi-3 with Azure AI Studio

After writing the code-first Phi-3 guide, I wanted to explain the same workflow from a different starting point.

Not every developer begins with scripts, local setup, and command-line steps. Some people understand the system faster when they can see the workflow in Azure AI Studio and Azure ML Studio: create the workspace, prepare compute, fine-tune the model, deploy it, and connect the result to Prompt Flow.

The challenge was to make the guided interface helpful without hiding the engineering underneath it. A low-code path should not make the system feel magical. It should make the sequence easier to see.

That was the purpose of this article. It showed the same architecture through a more visual path, while still keeping the reader aware of the resources, deployment steps, and integration points involved.

The lesson I took from it is that a good technical explanation meets developers where they start, then gradually leads them toward the underlying system.

Read the original article:

Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow in Azure AI Studio