metadata and md updates (Azure#2150)

Author: sdgilley

tutorials/README.md

@@ -40,9 +40,9 @@ Test Status is for branch - **_main_**
 |azureml-in-a-day|[azureml-in-a-day](azureml-in-a-day/azureml-in-a-day.ipynb)|Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.|[![azureml-in-a-day](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-azureml-in-a-day-azureml-in-a-day.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-azureml-in-a-day-azureml-in-a-day.yml)|
 |e2e-distributed-pytorch-image|[e2e-object-classification-distributed-pytorch](e2e-distributed-pytorch-image/e2e-object-classification-distributed-pytorch.ipynb)|Prepare data, test and run a multi-node multi-gpu pytorch job. Use mlflow to analyze your metrics|[![e2e-object-classification-distributed-pytorch](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-distributed-pytorch-image-e2e-object-classification-distributed-pytorch.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-distributed-pytorch-image-e2e-object-classification-distributed-pytorch.yml)|
 |e2e-ds-experience|[e2e-ml-workflow](e2e-ds-experience/e2e-ml-workflow.ipynb)|Create production ML pipelines with Python SDK v2 in a Jupyter notebook|[![e2e-ml-workflow](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-ds-experience-e2e-ml-workflow.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-ds-experience-e2e-ml-workflow.yml)|
-|get-started-notebooks|[cloud-workstation](get-started-notebooks/cloud-workstation.ipynb)|*no description*|[![cloud-workstation](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml)|
-|get-started-notebooks|[deploy-model](get-started-notebooks/deploy-model.ipynb)|*no description*|[![deploy-model](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml)|
-|get-started-notebooks|[explore-data](get-started-notebooks/explore-data.ipynb)|*no description*|[![explore-data](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml)|
+|get-started-notebooks|[cloud-workstation](get-started-notebooks/cloud-workstation.ipynb)|Notebook cells that accompany the Develop on cloud tutorial.|[![cloud-workstation](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml)|
+|get-started-notebooks|[deploy-model](get-started-notebooks/deploy-model.ipynb)|Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2.|[![deploy-model](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml)|
+|get-started-notebooks|[explore-data](get-started-notebooks/explore-data.ipynb)|Upload data to cloud storage, create a data asset, create new versions for data assets, use the data for interactive development.|[![explore-data](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml)|
 |get-started-notebooks|[pipeline](get-started-notebooks/pipeline.ipynb)|Create production ML pipelines with Python SDK v2 in a Jupyter notebook|[![pipeline](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-pipeline.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-pipeline.yml)|
 |get-started-notebooks|[quickstart](get-started-notebooks/quickstart.ipynb)|*no description*|[![quickstart](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-quickstart.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-quickstart.yml)|
 |get-started-notebooks|[train-model](get-started-notebooks/train-model.ipynb)|*no description*|[![train-model](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-train-model.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-train-model.yml)|

tutorials/azureml-getting-started/azureml-getting-started-studio.ipynb

@@ -414,7 +414,7 @@
    "description": "A quickstart tutorial to train and deploy an image classification model on Azure Machine Learning studio"
   },
   "kernelspec": {
-   "display_name": "Python 3.10 - SDK V2",
+   "display_name": "Python 3.10 - SDK v2",
    "language": "python",
    "name": "python310-sdkv2"
   },

tutorials/azureml-in-a-day/azureml-in-a-day.ipynb

@@ -802,9 +802,9 @@
    "description": "Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage."
   },
   "kernelspec": {
-   "display_name": "Python 3.10 - SDK V2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python310-sdkv2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -818,12 +818,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.10.6"
   },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "71a65d72c50a26f05c9d6876d6234594cb07dd1b273faf1eea7eaa26341f62bb"
-   }
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

tutorials/e2e-distributed-pytorch-image/e2e-object-classification-distributed-pytorch.ipynb

@@ -680,7 +680,7 @@
    "description": "Prepare data, test and run a multi-node multi-gpu pytorch job. Use mlflow to analyze your metrics"
   },
   "kernelspec": {
-   "display_name": "Python 3.10 - SDK V2",
+   "display_name": "Python 3.10 - SDK v2",
    "language": "python",
    "name": "python310-sdkv2"
   },

tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb

@@ -1326,7 +1326,7 @@
    "description": "Create production ML pipelines with Python SDK v2 in a Jupyter notebook"
   },
   "kernelspec": {
-   "display_name": "Python 3.10 - SDK V2",
+   "display_name": "Python 3.10 - SDK v2",
    "language": "python",
    "name": "python310-sdkv2"
   },

tutorials/get-started-notebooks/cloud-workstation.ipynb

@@ -210,6 +210,9 @@
   }
  ],
  "metadata": {
+  "description": {
+   "description": "Notebook cells that accompany the Develop on cloud tutorial."
+  },
   "kernel_info": {
    "name": "python310-sdkv2"
   },

tutorials/get-started-notebooks/deploy-model.ipynb

@@ -51,8 +51,6 @@
     "    \n",
     "    * If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "    \n",
-    "1. If you already completed the earlier training tutorial, [Train a model](https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-train-model), you can skip to the next prerequisite.\n",
-    "\n",
     "1. View your VM quota and ensure you have enough quota available to create online deployments. In this tutorial, you will need at least 8 cores of `STANDARD_DS3_v2` and 12 cores of `STANDARD_F4s_v2`. To view your VM quota usage and request quota increases, see [Manage resource quotas](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas#view-your-usage-and-quotas-in-the-azure-portal).\n",
     "\n",
     "## Set your kernel\n",
@@ -113,11 +111,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "> [!NOTE]\n",
-    "> Creating `MLClient` will not connect to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call (in this notebook, that will happen during compute creation).\n"
+    "> Creating `MLClient` will not connect to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call (this will happen in the next code cell).\n"
    ]
   },
   {
@@ -782,7 +781,7 @@
    "source": [
     "ml_client.online_deployments.begin_delete(\n",
     "    name=\"blue\", endpoint_name=online_endpoint_name\n",
-    ").wait()"
+    ").result()"
    ]
   },
   {
@@ -803,11 +802,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ml_client.online_endpoints.begin_delete(name=online_endpoint_name)"
+    "ml_client.online_endpoints.begin_delete(name=online_endpoint_name).result()"
    ]
   }
  ],
  "metadata": {
+  "description": {
+   "description": "Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2."
+  },
   "kernel_info": {
    "name": "python310-sdkv2"
   },

tutorials/get-started-notebooks/explore-data.ipynb

@@ -134,7 +134,7 @@
    "metadata": {},
    "source": [
     "> [!NOTE]\n",
-    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation)."
+    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen in the next code cell)."
    ]
   },
   {
@@ -165,7 +165,7 @@
     "\n",
     "The next notebook cell creates the data asset. The code sample uploads the raw data file to the designated cloud storage resource.  \n",
     "\n",
-    "Each time you create a data asset, you need a unique version for it.  If the version already exists, you'll get an error.  In this code, we're using time to generate a unique version, which will mostly work.  But if you happen to run this same cell on a different day at the exact same time, you'll get an error.  If this occurs, chances are good that it will be successful if you re-run the cell.\n",
+    "Each time you create a data asset, you need a unique version for it.  If the version already exists, you'll get an error.  In this code, we're using time to generate a unique version each time the cell is run.\n",
     "\n",
     "You can also omit the **version** parameter, and a version number is generated for you, starting with 1 and then incrementing from there. In this tutorial, we want to refer to specific version numbers, so we create a version number instead."
    ]
@@ -197,7 +197,9 @@
     "# local filesystem\n",
     "\n",
     "my_path = \"./data/default_of_credit_card_clients.csv\"\n",
-    "v1 = str(time.strftime(\"%H.%M.%S\", time.gmtime()))\n",
+    "# set the version number of the data asset to the current UTC time\n",
+    "v1 = time.strftime(\"%Y.%m.%d.%H%M%S\", time.gmtime())\n",
+    "\n",
     "\n",
     "my_data = Data(\n",
     "    name=\"credit-card\",\n",
@@ -525,6 +527,9 @@
   }
  ],
  "metadata": {
+  "description": {
+   "description": "Upload data to cloud storage, create a data asset, create new versions for data assets, use the data for interactive development."
+  },
   "kernel_info": {
    "name": "python310-sdkv2"
   },

tutorials/get-started-notebooks/pipeline.ipynb

@@ -112,11 +112,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "> [!NOTE]\n",
-    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation).\n",
+    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen when creating the `credit_data` data asset, two code cells from here).\n",
     "\n",
     "## Register data from an external url\n",
     "\n",

tutorials/get-started-notebooks/quickstart.ipynb

@@ -105,7 +105,7 @@
    "metadata": {},
    "source": [
     "> [!NOTE]\n",
-    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation)."
+    "> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in this notebook, that will happen in the cell that creates the compute cluster)."
    ]
   },
   {
@@ -704,7 +704,7 @@
   }
  ],
  "metadata": {
-  "description": "Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.",
+  "description": "Learn how a data scientist uses Azure Machine Learning to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.",
   "kernel_info": {
    "name": "python310-sdkv2"
   },
@@ -723,7 +723,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.6"
   },
   "microsoft": {
    "ms_spell_check": {