You are now ready to start training your tuned model. This is a batch process, and since it requires significant resources, your job may be queued for some time. Once accepted, it can take several hours to run, especially if you are working with a large, complex model and a large training data set. Azure AI Foundry tools allow you to view the status of a fine-tuning job, showing results, events, and hyperparameters used.
Each pass through the training data creates a checkpoint. This is a usable version of the model with the current debug state, so you can evaluate it with your code before finishing the fine-tuning task. You will always have access to the last three outputs to compare different versions before deploying your final choice.
Ensuring the safety of tuned models
Your tuned model is subject to Microsoft’s own AI security rules. It is not published until you explicitly choose to publish it, with testing and evaluation in private workspaces. At the same time, your training data remains private and is not stored with the model, reducing the risk of confidential data being leaked through fast attacks. Before use, Microsoft scans the training data to ensure it is free of malicious content, and if it finds unacceptable content, it aborts the job before it runs.