You’re now ready to start training your fine-tuned model. This is a batch process, and as it requires significant resources, your job may be queued for some time. Once accepted, a run can take several hours, especially if you are working with a large, complex model and a large training data set. Azure AI Foundry’s tools allow you to see the status of a fine-tuning job, showing results, events, and the hyperparameters used.
Each pass through the training data produces a checkpoint. This is a usable version of the model with the current state of tuning so you can evaluate them with your code before the fine-tuning job completes. You will always have access to the last three outputs so you can compare different versions before deploying your final choice.
Ensuring fine-tuned models are safe
Microsoft’s own AI safety rules apply to your fine-tuned model. It is not made public until you explicitly choose to publish it, with test and evaluation in private workspaces. At the same time, your training data stays private and is not stored alongside the model, reducing the risk of confidential data leaking through prompt attacks. Microsoft will scan training data before it’s used to ensure that it doesn’t have harmful content, and will abort a job before it runs if it finds unacceptable content.
