Fine-Tuning Phi-3 and Integrating It with Prompt Flow

This guide forced me to connect the full fine-tuning path instead of treating training as a standalone step.

A training command is only one piece of the work. Developers also need the surrounding path: prepare an Azure Machine Learning workspace, request GPU quota, set up the project, prepare the dataset, train the model, deploy it, and then integrate the custom model with Prompt Flow.

Writing the guide made the real shape of the problem clearer to me. Fine-tuning is often described as a model task, but in practice it is also a cloud workflow, a deployment workflow, and an integration workflow.

That distinction matters because each stage can fail for different reasons. A good tutorial has to help the reader move through the whole chain, not only the most exciting part.

The principle is still useful: custom model work is systems engineering. The model matters, but it is only one piece of the system that makes the model usable.

Read the original article:

Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide