Merge pull request #277242 from mrbullwinkle/mrb_06_05_2024_checkpoints

[Azure OpenAI] Checkpoints update

Author: ttorble

articles/ai-services/openai/includes/fine-tuning-openai-in-ai-studio.md

@@ -242,10 +242,18 @@ The result file is a CSV file that contains a header row and a row for each trai
 | `full_valid_loss` | The validation loss calculated at the end of each epoch. When training goes well, loss should decrease. |
 |`full_valid_mean_token_accuracy` | The valid mean token accuracy calculated at the end of each epoch. When training is going well, token accuracy should increase. |
 
-You can also view the data in your results.csv file as plots in Azure AI Studio under the **Metrics** tab of your fine-tuned model. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
+You can also view the data in your results.csv file as plots in Azure AI Studio under the **Metrics** tab of your fine-tuned model. Select the link for your trained model, and you will see two charts: loss, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
+
+:::image type="content" source="../media/fine-tuning/metrics.png" alt-text="Screenshot of metrics UI." lightbox="../media/fine-tuning/metrics.png":::
 
 Look for your loss to decrease over time, and your accuracy to increase. If you see a divergence between your training and validation data that may indicate that you are overfitting. Try training with fewer epochs, or a smaller learning rate multiplier.
 
+## Checkpoints
+
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
+
+:::image type="content" source="../media/fine-tuning/checkpoints.png" alt-text="Screenshot of checkpoints UI." lightbox="../media/fine-tuning/checkpoints.png":::
+
 ## Safety evaluation GPT-4 fine-tuning - public preview
 
 [!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]

articles/ai-services/openai/includes/fine-tuning-studio.md

@@ -317,6 +317,11 @@ Here are some of the tasks you can do on the **Models** pane:
 
 :::image type="content" source="../media/fine-tuning/studio-model-details.png" alt-text="Screenshot of the Models pane in Azure OpenAI Studio, with a custom model displayed." lightbox="../media/fine-tuning/studio-models-job-running.png":::
 
+## Checkpoints
+
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy. 
+
+
 ## Safety evaluation GPT-4 fine-tuning - public preview
 
 [!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]