Merge pull request #78 from MicrosoftDocs/main

Taojunshen · web-flow · commit ffd6c7e022e1 · 2024-08-31T05:56:37.000+08:00
8/30/2024 PM Publish
diff --git a/articles/ai-services/openai/how-to/batch.md b/articles/ai-services/openai/how-to/batch.md
@@ -6,7 +6,7 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: 
 ms.topic: how-to
-ms.date: 08/12/2024
+ms.date: 08/30/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
diff --git a/articles/ai-services/openai/includes/batch/batch-python.md b/articles/ai-services/openai/includes/batch/batch-python.md
@@ -61,6 +61,9 @@ The `custom_id` is required to allow you to identify which individual batch requ
 
 `model` attribute should be set to match the name of the Global Batch deployment you wish to target for inference responses.
 
+> [!IMPORTANT]
+> The `model` attribute must be set to match the name of the Global Batch deployment you wish to target for inference responses. The **same Global Batch model deployment name must be present on each line of the batch file.** If you want to target a different deployment you must do so in a separate batch file/job.
+
 ### Create input file
 
 For this article we'll create a file named `test.jsonl` and will copy the contents from standard input code block above to the file. You will need to modify and add your global batch deployment name to each line of the file. Save this file in the same directory that you're executing your Jupyter Notebook.
diff --git a/articles/ai-services/openai/includes/batch/batch-rest.md b/articles/ai-services/openai/includes/batch/batch-rest.md
@@ -50,6 +50,9 @@ The `custom_id` is required to allow you to identify which individual batch requ
 
 `model` attribute should be set to match the name of the Global Batch deployment you wish to target for inference responses.
 
+> [!IMPORTANT]
+> The `model` attribute must be set to match the name of the Global Batch deployment you wish to target for inference responses. The **same Global Batch model deployment name must be present on each line of the batch file.** If you want to target a different deployment you must do so in a separate batch file/job.
+
 ### Create input file
 
 For this article we'll create a file named `test.jsonl` and will copy the contents from standard input code block above to the file. You will need to modify and add your global batch deployment name to each line of the file.
diff --git a/articles/ai-services/openai/includes/batch/batch-studio.md b/articles/ai-services/openai/includes/batch/batch-studio.md
@@ -48,7 +48,10 @@ Like [fine-tuning](../../how-to/fine-tuning.md), global batch uses files in JSON
 
 The `custom_id` is required to allow you to identify which individual batch request corresponds to a given response. Responses won't be returned in identical order to the order defined in the `.jsonl` batch file.
 
-`model` attribute should be set to match the name of the Global Batch deployment you wish to target for inference responses.
+`model` attribute should be set to match the name of the Global Batch deployment you wish to target for inference responses. 
+
+> [!IMPORTANT]
+> The `model` attribute must be set to match the name of the Global Batch deployment you wish to target for inference responses. The **same Global Batch model deployment name must be present on each line of the batch file.** If you want to target a different deployment you must do so in a separate batch file/job.
 
 ### Create input file
 
diff --git a/articles/ai-services/openai/quotas-limits.md b/articles/ai-services/openai/quotas-limits.md
@@ -45,6 +45,7 @@ The following sections provide you with a quick guide to the default quotas and
 | Maximum number of Provisioned throughput units per deployment | 100,000 |
 | Max files per Assistant/thread | 10,000 when using the API or AI Studio. 20 when using Azure OpenAI Studio.|
 | Max file size for Assistants & fine-tuning | 512 MB |
+| Max size for all uploaded files for Assistants |100 GB |  
 | Assistants token limit | 2,000,000 token limit |
 | GPT-4o max images per request (# of images in the messages array/conversation history) | 10 |
 | GPT-4 `vision-preview` & GPT-4 `turbo-2024-04-09` default max tokens | 16 <br><br> Increase the `max_tokens` parameter value to avoid truncated responses. GPT-4o max tokens defaults to 4096. |
diff --git a/articles/ai-studio/how-to/create-manage-compute.md b/articles/ai-studio/how-to/create-manage-compute.md
@@ -18,7 +18,7 @@ author: sdgilley
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
-In this article, you learn how to create a compute instance in Azure AI Studio. You can create a compute instance in the Azure AI Studio or in the Azure portal. 
+In this article, you learn how to create a compute instance in Azure AI Studio. You can create a compute instance in the Azure AI Studio.
 
 You need a compute instance to:
 - Use prompt flow in Azure AI Studio. 
diff --git a/articles/ai-studio/how-to/create-projects.md b/articles/ai-studio/how-to/create-projects.md
@@ -117,8 +117,8 @@ In addition, a number of resources are only accessible by users in your project
    
    | Data connection | Storage location | Purpose |
    | --- | --- | --- |
-   | workspaceblobstore | {project-GUID}-blobstore | Default container for data upload |
-   | workspaceartifactstore | {project-GUID}-blobstore | Stores components and metadata for your project such as model weights |
+   | workspaceblobstore | {project-GUID}-azureml-blobstore | Default container for data upload |
+   | workspaceartifactstore | {project-GUID}-azureml | Stores components and metadata for your project such as model weights |
    | workspacefilestore | {project-GUID}-code | Hosts files created on your compute and using prompt flow |
 
 > [!NOTE]
diff --git a/articles/machine-learning/.openpublishing.redirection.machine-learning.json b/articles/machine-learning/.openpublishing.redirection.machine-learning.json
@@ -35,11 +35,6 @@
         "redirect_url": "/azure/machine-learning/tutorial-1st-experiment-bring-data",
         "redirect_document_id": true
     },
-    {
-        "source_path_from_root": "/articles/machine-learning/v-fake/introduction.md",
-        "redirect_url": "/azure/machine-learning/introduction",
-        "redirect_document_id": true
-    },
     {
         "source_path_from_root": "/articles/machine-learning/v-fake/how-to-use-secrets-in-runs.md",
         "redirect_url": "/azure/machine-learning/how-to-use-secrets-in-runs",
@@ -1472,12 +1467,12 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/service/tutorial-auto-train-models.md",
-        "redirect_url": "/azure/machine-learning/v1/how-to-auto-train-models-v1",
+        "redirect_url": "/azure/machine-learning/how-to-auto-train-models-v1",
         "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/tutorial-auto-train-models.md",
-        "redirect_url": "/azure/machine-learning/v1/how-to-auto-train-models-v1",
+        "redirect_url": "/azure/machine-learning/how-to-auto-train-models-v1",
         "redirect_document_id": false
     },
     {
@@ -3390,6 +3385,16 @@
         "redirect_url": "/azure/machine-learning/migrate-overview",
         "redirect_document_id": true
     },
+    {
+        "source_path_from_root": "/articles/machine-learning/v-fake/migrate-overview.md",
+        "redirect_url": "/azure/machine-learning/migrate-overview",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/v-fake/introduction.md",
+        "redirect_url": "/azure/machine-learning/introduction",
+        "redirect_document_id": true
+    },
     {
         "source_path_from_root": "/articles/machine-learning/classic/migrate-execute-r-script.md",
         "redirect_url": "/azure/machine-learning/migrate-execute-r-script",
@@ -4110,11 +4115,6 @@
         "redirect_url": "/azure/machine-learning/migrate-execute-r-script",
         "redirect_document_id": true
     },
-    {
-        "source_path_from_root": "/articles/machine-learning/v-fake/migrate-overview.md",
-        "redirect_url": "/azure/machine-learning/migrate-overview",
-        "redirect_document_id": false
-    },
     {
         "source_path_from_root": "/articles/machine-learning/v-fake/migrate-rebuild-experiment.md",
         "redirect_url": "/azure/machine-learning/migrate-rebuild-experiment",
diff --git a/articles/machine-learning/tutorial-automated-ml-forecast.md b/articles/machine-learning/tutorial-automated-ml-forecast.md
@@ -244,13 +244,9 @@ Delete just the deployment instance from the Azure Machine Learning studio, if y
 
 In this tutorial, you used automated ML in the Azure Machine Learning studio to create and deploy a time series forecasting model that predicts bike share rental demand. 
 
-See this article for steps on how to create a Power BI supported schema to facilitate consumption of your newly deployed web service:
-
-> [!div class="nextstepaction"]
-> [Consume a web service](/power-bi/connect-data/service-aml-integrate?context=azure%2fmachine-learning%2fcontext%2fml-context)
-
 + Learn more about [automated machine learning](concept-automated-ml.md).
 + For more information on classification metrics and charts, see the [Understand automated machine learning results](how-to-understand-automated-ml.md) article.
++ For more information on [FAQs on forecasting](how-to-automl-forecasting-faq.md).
 
 >[!NOTE]
 > This bike share dataset has been modified for this tutorial. This dataset was made available as part of a [Kaggle competition](https://www.kaggle.com/c/bike-sharing-demand/data) and was originally available via [Capital Bikeshare](https://www.capitalbikeshare.com/system-data). It can also be found within the [UCI Machine Learning Database](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset).<br><br>
diff --git a/articles/machine-learning/tutorial-first-experiment-automated-ml.md b/articles/machine-learning/tutorial-first-experiment-automated-ml.md
@@ -286,12 +286,9 @@ Delete just the deployment instance from Azure Machine Learning at https:\//ml.a
 
 In this automated machine learning tutorial, you used Azure Machine Learning's automated ML interface to create and deploy a classification model. See these articles for more information and next steps:
 
-> [!div class="nextstepaction"]
-> [Consume a web service](/power-bi/connect-data/service-aml-integrate?context=azure%2fmachine-learning%2fcontext%2fml-context)
-
 + Learn more about [automated machine learning](concept-automated-ml.md).
 + For more information on classification metrics and charts, see the [Understand automated machine learning results](how-to-understand-automated-ml.md) article.
-
++ Learn more about [how to set up AutoML for NLP](how-to-auto-train-nlp-models.md).
 
 >[!NOTE]
 > This Bank Marketing dataset is made available under the [Creative Commons (CCO: Public Domain) License](https://creativecommons.org/publicdomain/zero/1.0/). Any rights in individual contents of the database are licensed under the [Database Contents License](https://creativecommons.org/publicdomain/zero/1.0/) and available on [Kaggle](https://www.kaggle.com/datasets/janiobachmann/bank-marketing-dataset). This dataset was originally available within the [UCI Machine Learning Database](https://archive.ics.uci.edu/ml/datasets/bank+marketing).<br><br>
diff --git a/articles/machine-learning/v1/how-to-deploy-local.md b/articles/machine-learning/v1/how-to-deploy-local.md
@@ -247,8 +247,6 @@ You can then find your newly registered model on the Azure Machine Learning **Mo
 
 :::image type="content" source="media/how-to-deploy-local/registered-model.png" alt-text="Screenshot of Azure Machine Learning Model tab, showing an uploaded model." lightbox="media/how-to-deploy-local/registered-model.png":::
 
-For more information on uploading and updating models and environments, see [Register model and deploy locally with advanced usages](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb).
-
 ## Next steps
 
 - For information on using VS Code with Azure Machine Learning, see [Launch Visual Studio Code remotely connected to a compute instance (preview)](../how-to-launch-vs-code-remote.md)
diff --git a/articles/open-datasets/toc.yml b/articles/open-datasets/toc.yml
@@ -9,7 +9,7 @@
 - name: Tutorial
   items:
   - name: Regression with automated machine learning
-    href: ../machine-learning/tutorial-auto-train-models.md?bc=%2fazure%2fopen-datasets%2fbreadcrumb%2ftoc.json&toc=%2fazure%2fopen-datasets%2ftoc.json
+    href: /azure/machine-learning/how-to-auto-train-models-v1?bc=%2fazure%2fopen-datasets%2fbreadcrumb%2ftoc.json&toc=%2fazure%2fopen-datasets%2ftoc.json
   - name: Enrich an image classification model
     href: ../machine-learning/tutorial-train-models-with-aml.md?bc=%2fazure%2fopen-datasets%2fbreadcrumb%2ftoc.json&toc=%2fazure%2fopen-datasets%2ftoc.json
 - name: How-to guides
diff --git a/docfx.json b/docfx.json
@@ -19,6 +19,9 @@
         "dest": "."
       },
       {
+        "group": "grp-machine-learning",
+        "src": "articles/machine-learning",
+        "dest": "./machine-learning",
         "files": [
           "**/*.md",
           "**/*.yml"
@@ -29,12 +32,12 @@
           "**/obj/**",
           "**/v1/**",
           "**/v-fake/**"
-        ],
-        "src": "articles/machine-learning",
-        "dest": "./machine-learning",
-        "group": "grp-machine-learning"
+        ]
       },
       {
+        "group": "grp-document-intelligence",
+        "src": "articles/ai-services/document-intelligence",
+        "dest": "./ai-services/document-intelligence/",
         "files": [
           "**/*.md",
           "**/*.yml"
@@ -43,12 +46,12 @@
           "**/_themes/**",
           "**/includes/**",
           "**/obj/**"
-        ],
-        "src": "articles/ai-services/document-intelligence",
-        "dest": "./ai-services/document-intelligence/",
-        "group": "grp-document-intelligence"
+        ]
       },
       {
+        "group": "grp-machine-learningv1",
+        "src": "articles/machine-learning/v1",
+        "dest": "./machine-learning",
         "files": [
           "**/*.md",
           "**/*.yml"
@@ -57,10 +60,21 @@
           "**/_themes/**",
           "**/includes/**",
           "**/obj/**"
+        ]
+      },
+      {
+        "group": "grp-machine-learningv1",
+        "src": "articles/machine-learning/v-fake",
+        "dest": "./machine-learning/v1",        
+        "files": [
+          "**/*.md",
+          "**/*.yml"
         ],
-        "src": "articles/machine-learning/v1",
-        "dest": "./machine-learning",
-        "group": "grp-machine-learningv1"
+        "exclude": [
+          "**/_themes/**",
+          "**/includes/**",
+          "**/obj/**"
+        ]
       },
       {
         "files": [
@@ -90,6 +104,9 @@
         "dest": "."
       },
       {
+        "group": "grp-document-intelligence",
+        "src": "articles/ai-services/document-intelligence",
+        "dest": "./ai-services/document-intelligence/",
         "files": [
           "**/*.gif",
           "**/*.jpeg",
@@ -100,12 +117,12 @@
         "exclude": [
           "**/_themes/**",
           "**/obj/**"
-        ],
-        "src": "articles/ai-services/document-intelligence",
-        "dest": "./ai-services/document-intelligence/",
-        "group": "grp-document-intelligence"
+        ]
       },
       {
+        "group": "grp-machine-learning",
+        "src": "articles/machine-learning",
+        "dest": "./machine-learning/",
         "files": [
           "**/*.gif",
           "**/*.jpeg",
@@ -118,12 +135,12 @@
           "**/obj/**",
           "**/v1/**",
           "**/v-fake/**"
-        ],
-        "src": "articles/machine-learning",
-        "dest": "./machine-learning/",
-        "group": "grp-machine-learning"
+        ]
       },
       {
+        "group": "grp-machine-learningv1",
+        "src": "articles/machine-learning/v1",
+        "dest": "./machine-learning/",
         "files": [
           "**/*.gif",
           "**/*.jpeg",
@@ -134,10 +151,23 @@
         "exclude": [
           "**/_themes/**",
           "**/obj/**"
+        ]
+      },
+      {
+        "group": "grp-machine-learningv1",
+        "src": "articles/machine-learning/v-fake",
+        "dest": "./machine-learning/v1",
+        "files": [
+          "**/*.gif",
+          "**/*.jpeg",
+          "**/*.jpg",
+          "**/*.png",
+          "**/*.svg"
         ],
-        "src": "articles/machine-learning/v1",
-        "dest": "./machine-learning/",
-        "group": "grp-machine-learningv1"
+        "exclude": [
+          "**/_themes/**",
+          "**/obj/**"
+        ]
       }
     ],
     "groups": {
@@ -395,4 +425,4 @@
     "externalReference": [],
     "template": []
   }
-}
+}
