diff --git a/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb b/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb
index b4aed182f..175f2703d 100644
--- a/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb
+++ b/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb
@@ -29,6 +29,8 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
+        "# Vertex AI: Track parameters and metrics for custom training jobs\n",
+        "\n",
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
@@ -51,15 +53,6 @@
         "</table>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "j9gUDU_3vV9d"
-      },
-      "source": [
-        "# Vertex AI: Track parameters and metrics for custom training jobs"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -68,11 +61,15 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to track metrics and parameters for `Vertex AI` custom training jobs, and how to perform detailed analysis using this data.\n",
-        "\n",
-        "### Dataset\n",
-        "\n",
-        "This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone\n",
+        "This notebook demonstrates how to track metrics and parameters for `Vertex AI` custom training jobs, and how to perform detailed analysis using this data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "37147bd9c3c4"
+      },
+      "source": [
         "### Objective\n",
         "\n",
         "In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.\n",
@@ -85,8 +82,26 @@
         "The steps performed include:\n",
         "\n",
         "- Track parameters and metrics for a `Vertex AI` custom trained model.\n",
-        "- Extract and perform analysis for all parameters and metrics within an Experiment.\n",
+        "- Extract and perform analysis for all parameters and metrics within an Experiment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "96cb18467417"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c831245dc1d5"
+      },
+      "source": [
         "### Costs \n",
         "\n",
         "\n",
@@ -285,8 +300,7 @@
       },
       "outputs": [],
       "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
-        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "if PROJECT_ID == \"[your-project-id]\" or PROJECT_ID == \"\" or PROJECT_ID is None:\n",
         "    # Get your GCP project id from gcloud\n",
         "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
         "    PROJECT_ID = shell_output[0]\n",
@@ -332,7 +346,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -368,7 +385,7 @@
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Vertex AI Workbench**, your environment is already\n",
-        "authenticated. Skip this step."
+        "authenticated. "
       ]
     },
     {
@@ -664,9 +681,9 @@
         "id": "35QVNhACqcTJ"
       },
       "source": [
-        "### Create a managed tabular dataset from a CSV\n",
+        "### Create a Vertex AI Dataset from a CSV\n",
         "\n",
-        "A Managed dataset can be used to create an AutoML model or a custom model. "
+        "A Vertex AI Dataset can be used to create an AutoML model or a custom model. "
       ]
     },
     {
@@ -679,7 +696,7 @@
       "source": [
         "ds = aiplatform.TabularDataset.create(display_name=\"abalone\", gcs_source=[gcs_csv_path])\n",
         "\n",
-        "ds.resource_name"
+        "print(ds.resource_name)"
       ]
     },
     {
@@ -780,7 +797,11 @@
         "id": "k_QorXXztzPH"
       },
       "source": [
-        "Start a new experiment run to track training parameters and start the training job. Note that this operation will take around 10 mins."
+        "Start a new experiment run to track training parameters and start the training job. \n",
+        "\n",
+        "Prior to executing the training job, you call the `start_run()` method to initialize the start of the experiment, and then use the `log_params()` to log the parameters used in the experiment.\n",
+        "\n",
+        "*Note:* This operation will take around 10 mins."
       ]
     },
     {
@@ -818,7 +839,7 @@
         "id": "O-uCOL3Naap4"
       },
       "source": [
-        "Deploy model to Google Cloud. This operation will take 10-20 mins."
+        "Deploy model to Google Cloud. This operation may take a few minutes."
       ]
     },
     {
@@ -899,7 +920,7 @@
         "id": "_HphZ38obJeB"
       },
       "source": [
-        "Perform online prediction."
+        "### Perform online prediction"
       ]
     },
     {
@@ -911,7 +932,7 @@
       "outputs": [],
       "source": [
         "prediction = endpoint.predict(test_dataset.tolist())\n",
-        "prediction"
+        "print(prediction)"
       ]
     },
     {
@@ -920,7 +941,11 @@
         "id": "TDKiv_O7bNwE"
       },
       "source": [
-        "Calculate and track prediction evaluation metrics."
+        "### Calculate and track prediction evaluation metrics.\n",
+        "\n",
+        "Next, log the evaluation metrics for your experiment.\n",
+        "\n",
+        "Once the experiment is completed, you call the `end_run()` method to indicate the end of tracking for the experiment."
       ]
     },
     {
@@ -934,7 +959,9 @@
         "mse = mean_squared_error(test_labels, prediction.predictions)\n",
         "mae = mean_absolute_error(test_labels, prediction.predictions)\n",
         "\n",
-        "aiplatform.log_metrics({\"mse\": mse, \"mae\": mae})"
+        "aiplatform.log_metrics({\"mse\": mse, \"mae\": mae})\n",
+        "\n",
+        "aiplatform.end_run()"
       ]
     },
     {