Merge pull request #4931 from voutilad/global-training

Update Fine Tuning walkthrough and add Global Training

Author: v-regandowner

.gitignore

@@ -11,6 +11,8 @@ _repo.*/
 
 .openpublishing.buildcore.ps1
 
+*sec.endpointdlp
+
 # CoPilot instructions and prompts
 .github/copilot-instructions.md
-.github/prompts/*.md
+.github/prompts/*.md

articles/ai-services/openai/includes/fine-tune-models.md

@@ -14,13 +14,29 @@ manager: nitinme
 > `gpt-35-turbo` - Fine-tuning of this model is limited to a subset of regions, and isn't available in every region the base model is available. 
 >
 > The supported regions for fine-tuning might vary if you use Azure OpenAI models in an Azure AI Foundry project versus outside a project.
+>
+
+|  Model ID  | Standard Training Regions | Global Training (Preview) | Max request (tokens) | Training Data (up to) | Modality |
+|  --- | --- | :---: | :---: | :---: | --- |
+| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | - | Input: 16,385<br> Output: 4,096 |  Sep 2021 | Text to Text |
+| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | - | 16,385 | Sep 2021 | Text to Text |
+| `gpt-4o-mini` (2024-07-18) | North Central US <br> Sweden Central | - | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 65,536 | Oct 2023 | Text to Text |
+| `gpt-4o` (2024-08-06) | East US2 <br> North Central US <br> Sweden Central | - | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 65,536 | Oct 2023 | Text & Vision to Text |
+| `gpt-4.1` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text & Vision to Text |
+| `gpt-4.1-mini` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text to Text |
+| `gpt-4.1-nano` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 32,768 | May 2024 | Text to Text |
 
-|  Model ID  | Fine-tuning regions | Max request (tokens) | Training Data (up to) | Modality |
-|  --- | --- | :---: | :---: | --- |
-| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | Input: 16,385<br> Output: 4,096 |  Sep 2021 | Text to Text |
-| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 | Text to Text |
-| `gpt-4o-mini` (2024-07-18) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 65,536 | Oct 2023 | Text to Text |
-| `gpt-4o` (2024-08-06) | East US2 <br> North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 65,536 | Oct 2023 | Text & Vision to Text |
-| `gpt-4.1` (2025-04-14) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text & Vision to Text |
-| `gpt-4.1-mini` (2025-04-14) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text to Text |
-| `gpt-4.1-nano` (2025-04-14) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 32,768 | May 2024 | Text to Text |
+> [!NOTE]
+> **Global** training (in Public Preview) provides [more affordable](https://aka.ms/aoai-pricing) training per-token, but does not offer [data residency](https://aka.ms/data-residency). It is currently available to Azure OpenAI resources in the following regions, with more regions coming soon:
+>- Australia East
+>- Brazil South
+>- France Central
+>- Germany West Central
+>- Italy North
+>- Japan East _(no vision support)_
+>- Korea Central
+>- Norway East
+>- Poland Central
+>- Southeast Asia
+>- Spain Central
+>- South Africa North

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

@@ -9,12 +9,9 @@
 ms.custom: include, build-2024
 ---
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
-
 ## Prerequisites
 
 - Read the [When to use Azure OpenAI fine-tuning guide](../concepts/fine-tuning-considerations.md).
-
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
 - An [Azure AI project](../../../ai-foundry/how-to/create-projects.md) in Azure AI Foundry portal.
 - An [Azure OpenAI connection](/azure/ai-foundry/how-to/connections-add?tabs=azure-openai#connection-details) to a resource in a [region where fine-tuning is supported](/azure/ai-services/openai/concepts/models#fine-tuning-models).
@@ -25,26 +22,18 @@ ms.custom: include, build-2024
 
 ### Supported models
 
-The following models support fine-tuning:
-
-- `gpt-35-turbo` (1106)
-- `gpt-35-turbo` (0125)
-- `gpt-4o` (2024-08-06)
-- `gpt-4o-mini` (2024-07-18)
-- `gpt-4.1` (2024-04-14)
-- `gpt-4.1-mini`(2025-04-14)
+Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
 
 Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
 
-Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
-
 ## Review the workflow for Azure AI Foundry
 
 Take a moment to review the fine-tuning workflow for using Azure AI Foundry:
 
 1. Prepare your training and validation data.
-1. Use the **Fine-tune model** wizard in Azure AI Foundry portal to train your custom model.
-    1. Select a model to finetune.
+1. Use the **Create a fine-tuned model** wizard in Azure AI Foundry portal to train your custom model.
+    1. Select a training method.
+    1. Select a base model.
     1. [Choose your training data](#choose-your-training-data).
     1. Optionally, [choose your validation data](#choose-your-validation-data).
     1. Optionally, [configure your parameters](#configure-your-parameters) for your fine-tuning job.

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

@@ -20,23 +20,15 @@ ms.author: mbullwin
 - Fine-tuning access requires **Cognitive Services OpenAI Contributor**.
 - If you do not already have access to view quota, and deploy models in Azure AI Foundry portal you will require [additional permissions](../how-to/role-based-access-control.md).  
 
+> [!NOTE]
+> The REST API does not currently support **Global** training jobs. Inspecting Global training jobs via the API may return incorrect information.
 
 ### Supported models
 
-The following models support fine-tuning:
-
-- `gpt-35-turbo` (1106)
-- `gpt-35-turbo` (0125)
-- `gpt-4o` (2024-08-06)
-- `gpt-4o-mini` (2024-07-18)
-- `gpt-4.1` (2024-04-14)
-- `gpt-4.1-mini`(2025-04-14)
+Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
 
 Or you can fine tune a previously fine-tuned model, formatted as `base-model.ft-{jobid}`.
 
-
-Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
-
 ## Review the workflow for the Python SDK
 
 Take a moment to review the fine-tuning workflow for using the Python SDK with Azure OpenAI:
@@ -155,7 +147,7 @@ In this example we are also passing the seed parameter. The seed controls the re
 response = client.fine_tuning.jobs.create(
     training_file=training_file_id,
     validation_file=validation_file_id,
-    model="gpt-35-turbo-0125", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+    model="gpt-4.1-2025-04-14", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters.
     seed = 105  # seed parameter controls reproducibility of the fine-tuning job. If no seed is specified one will be generated automatically.
 )
 
@@ -193,7 +185,7 @@ client = AzureOpenAI(
 
 client.fine_tuning.jobs.create(
   training_file="file-abc123", 
-  model="gpt-35-turbo-0125", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+  model="gpt-4.1-2025-04-14", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters.
   hyperparameters={
     "n_epochs":2
   }
@@ -278,13 +270,64 @@ Once you're satisfied with the metrics from your fine-tuning job, or you just wa
 
 If you're deploying for further validation, consider deploying for [testing](../how-to/fine-tune-test.md?tabs=python) using a Developer deployment.
 
+Unlike the previous SDK commands, deployment must be done using the control plane API which requires separate authorization, a different API path, and a different API version.
+
+|variable      | Definition|
+|--------------|-----------|
+| token        | There are multiple ways to generate an authorization token. The easiest method for initial testing is to launch the Cloud Shell from the [Azure portal](https://portal.azure.com). Then run [`az account get-access-token`](/cli/azure/account#az-account-get-access-token()). You can use this token as your temporary authorization token for API testing. We recommend storing this in a new environment variable. |
+| subscription | The subscription ID for the associated Azure OpenAI resource. |
+| resource_group | The resource group name for your Azure OpenAI resource. |
+| resource_name | The Azure OpenAI resource name. |
+| model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-4.1-2025-04-14.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+
+```python
+import json
+import os
+import requests
+
+token= os.getenv("<TOKEN>") 
+subscription = "<YOUR_SUBSCRIPTION_ID>"  
+resource_group = "<YOUR_RESOURCE_GROUP_NAME>"
+resource_name = "<YOUR_AZURE_OPENAI_RESOURCE_NAME>"
+model_deployment_name ="gpt-41-ft" # custom deployment name that you will use to reference the model when making inference calls.
+
+deploy_params = {'api-version': "2024-10-01"} # control plane API version rather than dataplane API for this call 
+deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}
+
+deploy_data = {
+    "sku": {"name": "standard", "capacity": 1}, 
+    "properties": {
+        "model": {
+            "format": "OpenAI",
+            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-4.1-2025-04-14.ft-b044a9d3cf9c4228b5d393567f693b83
+            "version": "1"
+        }
+    }
+}
+deploy_data = json.dumps(deploy_data)
+
+request_url = f'https://management.azure.com/subscriptions/{subscription}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{resource_name}/deployments/{model_deployment_name}'
+
+print('Creating a new deployment...')
+
+r = requests.put(request_url, params=deploy_params, headers=deploy_headers, data=deploy_data)
+
+print(r)
+print(r.reason)
+print(r.json())
+
+```
+
+Learn more about cross region deployment and use the deployed model [here](../how-to/fine-tuning-deploy.md#use-your-deployed-fine-tuned-model).
+
 If you're ready to deploy for production or have particular data residency needs, follow our [deployment guide](../how-to/fine-tuning-deploy.md?tabs=python).
 
 ## Continuous fine-tuning
 
 Once you have created a fine-tuned model you might want to continue to refine the model over time through further fine-tuning. Continuous fine-tuning is the iterative process of selecting an already fine-tuned model as a base model and fine-tuning it further on new sets of training examples.
 
-To perform fine-tuning on a model that you have previously fine-tuned you would use the same process as described in [create a customized model](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
+To perform fine-tuning on a model that you have previously fine-tuned you would use the same process as described in [create a customized model](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-4.1-2025-04-14.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
 
 ```python
 from openai import AzureOpenAI
@@ -298,7 +341,7 @@ client = AzureOpenAI(
 response = client.fine_tuning.jobs.create(
     training_file=training_file_id,
     validation_file=validation_file_id,
-    model="gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7" # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+    model="gpt-4.1-2025-04-14.ft-5fd1918ee65d4cd38a5dcf6835066ed7"
 )
 
 job_id = response.id

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

@@ -18,23 +18,14 @@ ms.author: mbullwin
 - Fine-tuning access requires **Cognitive Services OpenAI Contributor**.
 - If you don't already have access to view quota, and deploy models in Azure AI Foundry portal you'll require [additional permissions](../how-to/role-based-access-control.md).  
 
-
+> [!NOTE]
+> The REST API does not currently support **Global** training jobs. Inspecting Global training jobs via the API may return incorrect information.
 
 ### Supported models
 
-The following models support fine-tuning:
-
-- `gpt-35-turbo` (1106)
-- `gpt-35-turbo` (0125)
-- `gpt-4o` (2024-08-06)
-- `gpt-4o-mini` (2024-07-18)
-- `gpt-4.1` (2024-04-14)
-- `gpt-4.1-mini`(2025-04-14)
-
-Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
-
 Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
 
+Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
 
 
 ## Review the workflow for the REST API
@@ -138,7 +129,7 @@ curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2024-10-
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{
-    "model": "gpt-35-turbo-0125", 
+    "model": "gpt-4.1-2025-04-14",
     "training_file": "<TRAINING_FILE_ID>", 
     "validation_file": "<VALIDATION_FILE_ID>",
     "seed": 105
@@ -229,20 +220,47 @@ If you're deploying for further validation, consider deploying for [testing](../
 
 If you're ready to deploy for production or have particular data residency needs, follow our [deployment guide](../how-to/fine-tuning-deploy.md?tabs=rest).
 
+|variable      | Definition|
+|--------------|-----------|
+| token        | There are multiple ways to generate an authorization token. The easiest method for initial testing is to launch the Cloud Shell from the [Azure portal](https://portal.azure.com). Then run [`az account get-access-token`](/cli/azure/account#az-account-get-access-token()). You can use this token as your temporary authorization token for API testing. We recommend storing this in a new environment variable. |
+| subscription | The subscription ID for the associated Azure OpenAI resource. |
+| resource_group | The resource group name for your Azure OpenAI resource. |
+| resource_name | The Azure OpenAI resource name. |
+| model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-4.1-2025-04-14.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+
+```bash
+curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
+  -H "Authorization: Bearer <TOKEN>" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "sku": {"name": "standard", "capacity": 1},
+    "properties": {
+        "model": {
+            "format": "OpenAI",
+            "name": "<FINE_TUNED_MODEL>",
+            "version": "1"
+        }
+    }
+}'
+```
+
+Learn more about cross region deployment and use the deployed model [here](../how-to/fine-tuning-deploy.md#use-your-deployed-fine-tuned-model).
+
 
 ## Continuous fine-tuning
 
 Once you have created a fine-tuned model, you might want to continue to refine the model over time through further fine-tuning. Continuous fine-tuning is the iterative process of selecting an already fine-tuned model as a base model and fine-tuning it further on new sets of training examples.
 
-To perform fine-tuning on a model that you have previously fine-tuned, you would use the same process as described in [create a customized model](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
+To perform fine-tuning on a model that you have previously fine-tuned, you would use the same process as described in [create a customized model](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-4.1-2025-04-14.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
 
 ```bash
 curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2023-12-01-preview \
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{
-    "model": "gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7", 
-    "training_file": "<TRAINING_FILE_ID>", 
+    "model": "gpt-4.1-2025-04-14.ft-5fd1918ee65d4cd38a5dcf6835066ed7",
+    "training_file": "<TRAINING_FILE_ID>",
     "validation_file": "<VALIDATION_FILE_ID>",
     "suffix": "<additional text used to help identify fine-tuned models>"
 }'