update samples from Release-160 as a part of 1.0.76 SDK release

Author: vizhur

.amlignore

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.74.1 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.76 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

.vscode/settings.json

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -29,8 +36,6 @@
         "- Register the pretrained MNIST model into the model registry. \n",
         "- Use the registered model to do batch inference on the images in the data blob container.\n",
         "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)\n",
-        "\n",
         "## Prerequisites\n",
         "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
@@ -485,7 +490,7 @@
       "source": [
         "## Cleanup Compute resources\n",
         "\n",
-        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
+        "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
       ]
     },
     {
@@ -514,6 +519,27 @@
         "name": "tracych"
       }
     ],
+    "friendly_name": "MNIST data inferencing using ParallelRunStep",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "None"
+    ],
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Digit identification",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",