fix: auto review (#800)

* feat: tune template

* feat: tune template

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auto review

notebooks/community/ml_ops/stage2/get_started_automl_training.ipynb

@@ -89,52 +89,6 @@
         "- Train a video model"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:flowers,icn"
-      },
-      "source": [
-        "### Datasets\n",
-        "\n",
-        "#### Image\n",
-        "\n",
-        "The image dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower in a given image from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:gsod,lrg"
-      },
-      "source": [
-        "#### Tabular\n",
-        "\n",
-        "The tabular dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:happydb,tcn"
-      },
-      "source": [
-        "#### Text\n",
-        "\n",
-        "The text dataset used for this tutorial is the [Happy Moments dataset](https://www.kaggle.com/ritresearch/happydb) from [Kaggle Datasets](https://www.kaggle.com/ritresearch/happydb). The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "98eb93ec6faa"
-      },
-      "source": [
-        "#### Video\n",
-        "\n",
-        "The video dataset used for this tutorial is the golf swing recognition portion of the [Human Motion dataset](https://todo) from [MIT](http://cbcl.mit.edu/publications/ps/Kuehne_etal_iccv11.pdf). The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket. The trained model will predict the start frame where a golf swing begins."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage2/get_started_vertex_training_pytorch.ipynb

@@ -106,7 +106,7 @@
         "id": "85ee859437ed"
       },
       "source": [
-        "## Costs \n",
+        "### Costs \n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",

notebooks/community/ml_ops/stage2/get_started_vertex_vizier.ipynb

@@ -87,17 +87,6 @@
         "- Hyperparameter tuning with Vizier (Bayesian) algorithm."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:custom,boston,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -134,6 +123,38 @@
         "- multiple of objectives"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:custom,boston,lrg"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c480fc50ec3c"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
+        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {