diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md
index 1b926eeca5..b5fe7bad4b 100644
--- a/docs/getting-started/quickstart.md
+++ b/docs/getting-started/quickstart.md
@@ -377,6 +377,14 @@ pprint(feature_vector)
 
 You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found [here](https://docs.feast.dev/getting-started/concepts/feature-retrieval).
 
+The `driver_activity` feature service pulls all features from the `driver_hourly_stats` feature view:
+
+```python
+driver_stats_fs = FeatureService(
+    name="driver_activity", features=[driver_hourly_stats_view]
+)
+```
+
 {% tabs %}
 {% tab title="Python" %}
 ```python
@@ -384,7 +392,7 @@ from feast import FeatureStore
 feature_store = FeatureStore('.')  # Initialize the feature store
 
 feature_service = feature_store.get_feature_service("driver_activity")
-features = feature_store.get_online_features(
+feature_vector = feature_store.get_online_features(
     features=feature_service,
     entity_rows=[
         # {join_key: entity_value}
@@ -392,6 +400,7 @@ features = feature_store.get_online_features(
         {"driver_id": 1005},
     ],
 ).to_dict()
+pprint(feature_vector)
 ```
 
 {% tabs %}
diff --git a/examples/quickstart/quickstart.ipynb b/examples/quickstart/quickstart.ipynb
index 60974d2751..d29ee4fa35 100644
--- a/examples/quickstart/quickstart.ipynb
+++ b/examples/quickstart/quickstart.ipynb
@@ -1,20 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Feast Codelab",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -54,28 +38,20 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "rXNMAAJKQPG5",
-        "outputId": "52297709-380b-4200-8e7c-3d0102a82ea4"
+        "outputId": "94fb2260-4453-45c9-ba77-5b384823a621"
       },
+      "outputs": [],
       "source": [
         "%%sh\n",
         "pip install feast -U -q\n",
         "pip install Pygments -q\n",
         "echo \"Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\""
-      ],
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\n"
-          ]
-        }
       ]
     },
     {
@@ -107,28 +83,32 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IhirSkgUvYau",
-        "outputId": "df90af1a-06bd-48a1-94e6-7def19e87d5f"
+        "outputId": "664367b9-6a2a-493d-fd78-6495fb459fa2"
       },
-      "source": [
-        "!feast init feature_repo"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Feast is an open source project that collects anonymized error reporting and usage statistics. To opt out or learn more see https://docs.feast.dev/reference/usage\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
             "\n",
             "Creating a new Feast repository in \u001b[1m\u001b[32m/content/feature_repo\u001b[0m.\n",
             "\n"
           ]
         }
+      ],
+      "source": [
+        "!feast init feature_repo"
       ]
     },
     {
@@ -150,22 +130,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "9jXuzt4ovzA3",
-        "outputId": "bff15f0c-9f8e-4a3c-e605-5ad84be30709"
+        "outputId": "9e326892-f0cc-4d86-d0b2-f33f822f83a9"
       },
-      "source": [
-        "%cd feature_repo\n",
-        "!ls -R"
-      ],
-      "execution_count": 2,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "/content/feature_repo\n",
             ".:\n",
@@ -175,6 +151,10 @@
             "driver_stats.parquet\n"
           ]
         }
+      ],
+      "source": [
+        "%cd feature_repo\n",
+        "!ls -R"
       ]
     },
     {
@@ -198,21 +178,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "9_YJ--uYdtcP",
-        "outputId": "89268e31-6be0-43fb-e576-6d335a2c1dd9"
+        "outputId": "af56a8da-9ca2-4dd9-f73c-a60dd3e1613a"
       },
-      "source": [
-        "!pygmentize feature_store.yaml"
-      ],
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[94mproject\u001b[39;49;00m: feature_repo\n",
             "\u001b[94mregistry\u001b[39;49;00m: data/registry.db\n",
@@ -221,6 +198,9 @@
             "    \u001b[94mpath\u001b[39;49;00m: data/online_store.db\n"
           ]
         }
+      ],
+      "source": [
+        "!pygmentize feature_store.yaml"
       ]
     },
     {
@@ -236,56 +216,21 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 424
+          "height": 423
         },
         "id": "sIF2lO59dwzi",
-        "outputId": "80e798d5-df21-4ebd-de1c-9bde282bd742"
+        "outputId": "8931930b-b32f-43e1-d45b-de230489c7b8"
       },
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "pd.read_parquet(\"data/driver_stats.parquet\")"
-      ],
-      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "               event_timestamp  driver_id  conv_rate  acc_rate  \\\n",
-              "0    2022-03-31 14:00:00+00:00       1005   0.313336  0.231481   \n",
-              "1    2022-03-31 15:00:00+00:00       1005   0.959499  0.942614   \n",
-              "2    2022-03-31 16:00:00+00:00       1005   0.231786  0.313516   \n",
-              "3    2022-03-31 17:00:00+00:00       1005   0.886911  0.531613   \n",
-              "4    2022-03-31 18:00:00+00:00       1005   0.574945  0.718223   \n",
-              "...                        ...        ...        ...       ...   \n",
-              "1802 2022-04-15 12:00:00+00:00       1001   0.521622  0.266667   \n",
-              "1803 2022-04-15 13:00:00+00:00       1001   0.003188  0.535501   \n",
-              "1804 2021-04-12 07:00:00+00:00       1001   0.709081  0.823138   \n",
-              "1805 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
-              "1806 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
-              "\n",
-              "      avg_daily_trips                 created  \n",
-              "0                 303 2022-04-15 14:34:10.056  \n",
-              "1                 842 2022-04-15 14:34:10.056  \n",
-              "2                 782 2022-04-15 14:34:10.056  \n",
-              "3                 634 2022-04-15 14:34:10.056  \n",
-              "4                 441 2022-04-15 14:34:10.056  \n",
-              "...               ...                     ...  \n",
-              "1802              406 2022-04-15 14:34:10.056  \n",
-              "1803              593 2022-04-15 14:34:10.056  \n",
-              "1804              997 2022-04-15 14:34:10.056  \n",
-              "1805              534 2022-04-15 14:34:10.056  \n",
-              "1806              534 2022-04-15 14:34:10.056  \n",
-              "\n",
-              "[1807 rows x 6 columns]"
-            ],
             "text/html": [
               "\n",
-              "  <div id=\"df-fdad8041-cb31-4675-af7e-2643a4371f51\">\n",
+              "  <div id=\"df-5e62c34a-e5aa-4230-bfc9-685de97aaec5\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -316,48 +261,48 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>2022-03-31 14:00:00+00:00</td>\n",
+              "      <td>2022-05-30 19:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.313336</td>\n",
-              "      <td>0.231481</td>\n",
-              "      <td>303</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.061944</td>\n",
+              "      <td>0.517414</td>\n",
+              "      <td>467</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>2022-03-31 15:00:00+00:00</td>\n",
+              "      <td>2022-05-30 20:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.959499</td>\n",
-              "      <td>0.942614</td>\n",
-              "      <td>842</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.265881</td>\n",
+              "      <td>0.636260</td>\n",
+              "      <td>709</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2022-03-31 16:00:00+00:00</td>\n",
+              "      <td>2022-05-30 21:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.231786</td>\n",
-              "      <td>0.313516</td>\n",
-              "      <td>782</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.830253</td>\n",
+              "      <td>0.461117</td>\n",
+              "      <td>731</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>2022-03-31 17:00:00+00:00</td>\n",
+              "      <td>2022-05-30 22:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.886911</td>\n",
-              "      <td>0.531613</td>\n",
-              "      <td>634</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.828208</td>\n",
+              "      <td>0.520325</td>\n",
+              "      <td>919</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>2022-03-31 18:00:00+00:00</td>\n",
+              "      <td>2022-05-30 23:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.574945</td>\n",
-              "      <td>0.718223</td>\n",
-              "      <td>441</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.375715</td>\n",
+              "      <td>0.084719</td>\n",
+              "      <td>874</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -370,54 +315,54 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1802</th>\n",
-              "      <td>2022-04-15 12:00:00+00:00</td>\n",
+              "      <td>2022-06-14 17:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.521622</td>\n",
-              "      <td>0.266667</td>\n",
-              "      <td>406</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.016256</td>\n",
+              "      <td>0.293051</td>\n",
+              "      <td>908</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1803</th>\n",
-              "      <td>2022-04-15 13:00:00+00:00</td>\n",
+              "      <td>2022-06-14 18:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.003188</td>\n",
-              "      <td>0.535501</td>\n",
-              "      <td>593</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.651631</td>\n",
+              "      <td>0.855919</td>\n",
+              "      <td>685</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1804</th>\n",
               "      <td>2021-04-12 07:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.709081</td>\n",
-              "      <td>0.823138</td>\n",
-              "      <td>997</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.828805</td>\n",
+              "      <td>0.375509</td>\n",
+              "      <td>106</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1805</th>\n",
-              "      <td>2022-04-08 02:00:00+00:00</td>\n",
+              "      <td>2022-06-07 07:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.033297</td>\n",
-              "      <td>0.053268</td>\n",
-              "      <td>534</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.324065</td>\n",
+              "      <td>0.970185</td>\n",
+              "      <td>824</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1806</th>\n",
-              "      <td>2022-04-08 02:00:00+00:00</td>\n",
+              "      <td>2022-06-07 07:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.033297</td>\n",
-              "      <td>0.053268</td>\n",
-              "      <td>534</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.324065</td>\n",
+              "      <td>0.970185</td>\n",
+              "      <td>824</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "<p>1807 rows × 6 columns</p>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdad8041-cb31-4675-af7e-2643a4371f51')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e62c34a-e5aa-4230-bfc9-685de97aaec5')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -468,12 +413,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51 button.colab-df-convert');\n",
+              "          document.querySelector('#df-5e62c34a-e5aa-4230-bfc9-685de97aaec5 button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51');\n",
+              "          const element = document.querySelector('#df-5e62c34a-e5aa-4230-bfc9-685de97aaec5');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -493,11 +438,46 @@
               "    </div>\n",
               "  </div>\n",
               "  "
+            ],
+            "text/plain": [
+              "               event_timestamp  driver_id  conv_rate  acc_rate  \\\n",
+              "0    2022-05-30 19:00:00+00:00       1005   0.061944  0.517414   \n",
+              "1    2022-05-30 20:00:00+00:00       1005   0.265881  0.636260   \n",
+              "2    2022-05-30 21:00:00+00:00       1005   0.830253  0.461117   \n",
+              "3    2022-05-30 22:00:00+00:00       1005   0.828208  0.520325   \n",
+              "4    2022-05-30 23:00:00+00:00       1005   0.375715  0.084719   \n",
+              "...                        ...        ...        ...       ...   \n",
+              "1802 2022-06-14 17:00:00+00:00       1001   0.016256  0.293051   \n",
+              "1803 2022-06-14 18:00:00+00:00       1001   0.651631  0.855919   \n",
+              "1804 2021-04-12 07:00:00+00:00       1001   0.828805  0.375509   \n",
+              "1805 2022-06-07 07:00:00+00:00       1003   0.324065  0.970185   \n",
+              "1806 2022-06-07 07:00:00+00:00       1003   0.324065  0.970185   \n",
+              "\n",
+              "      avg_daily_trips                 created  \n",
+              "0                 467 2022-06-14 19:00:52.584  \n",
+              "1                 709 2022-06-14 19:00:52.584  \n",
+              "2                 731 2022-06-14 19:00:52.584  \n",
+              "3                 919 2022-06-14 19:00:52.584  \n",
+              "4                 874 2022-06-14 19:00:52.584  \n",
+              "...               ...                     ...  \n",
+              "1802              908 2022-06-14 19:00:52.584  \n",
+              "1803              685 2022-06-14 19:00:52.584  \n",
+              "1804              106 2022-06-14 19:00:52.584  \n",
+              "1805              824 2022-06-14 19:00:52.584  \n",
+              "1806              824 2022-06-14 19:00:52.584  \n",
+              "\n",
+              "[1807 rows x 6 columns]"
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 4
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "pd.read_parquet(\"data/driver_stats.parquet\")"
       ]
     },
     {
@@ -524,27 +504,24 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "DPqXCoNpL0SX",
-        "outputId": "be1308b2-0c83-4dd3-eb88-e79ffcbd20d6"
+        "outputId": "a31a40c4-e60a-4f62-ae1c-227ce0aedea4"
       },
-      "source": [
-        "!pygmentize -f terminal16m example.py"
-      ],
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[38;2;64;128;128;03m# This is an example feature definition file\u001b[39;00m\n",
             "\n",
             "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mdatetime\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m timedelta\n",
             "\n",
-            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, FeatureView, Field, FileSource, ValueType\n",
+            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, FeatureService, FeatureView, Field, FileSource, ValueType\n",
             "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m\u001b[38;2;0;0;255;01m.\u001b[39;00m\u001b[38;2;0;0;255;01mtypes\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Float32, Int64\n",
             "\n",
             "\u001b[38;2;64;128;128;03m# Read data from parquet files. Parquet is convenient for local development mode. For\u001b[39;00m\n",
@@ -558,7 +535,7 @@
             "\n",
             "\u001b[38;2;64;128;128;03m# Define an entity for the driver. You can think of entity as a primary key used to\u001b[39;00m\n",
             "\u001b[38;2;64;128;128;03m# fetch features.\u001b[39;00m\n",
-            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64, join_key\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,)\n",
+            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, join_keys\u001b[38;2;102;102;102m=\u001b[39m[\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m], value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64,)\n",
             "\n",
             "\u001b[38;2;64;128;128;03m# Our parquet files contain sample data that includes a driver_id column, timestamps and\u001b[39;00m\n",
             "\u001b[38;2;64;128;128;03m# three feature column. Here we define a Feature View that will allow us to serve this\u001b[39;00m\n",
@@ -575,9 +552,16 @@
             "    online\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;0;128;0;01mTrue\u001b[39;00m,\n",
             "    source\u001b[38;2;102;102;102m=\u001b[39mdriver_hourly_stats,\n",
             "    tags\u001b[38;2;102;102;102m=\u001b[39m{},\n",
+            ")\n",
+            "\n",
+            "driver_stats_fs \u001b[38;2;102;102;102m=\u001b[39m FeatureService(\n",
+            "    name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_activity\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, features\u001b[38;2;102;102;102m=\u001b[39m[driver_hourly_stats_view]\n",
             ")\n"
           ]
         }
+      ],
+      "source": [
+        "!pygmentize -f terminal16m example.py"
       ]
     },
     {
@@ -592,29 +576,34 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "RYKCKKrcxYZG",
-        "outputId": "9745d7eb-b4b8-4a43-bf47-189bbf07ae09"
+        "outputId": "f34aa509-1dc6-4e50-e8ee-12897138f3b9"
       },
-      "source": [
-        "!feast apply"
-      ],
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Created entity \u001b[1m\u001b[32mdriver_id\u001b[0m\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
+            "Created entity \u001b[1m\u001b[32mdriver\u001b[0m\n",
             "Created feature view \u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m\n",
+            "Created feature service \u001b[1m\u001b[32mdriver_activity\u001b[0m\n",
             "\n",
             "Created sqlite table \u001b[1m\u001b[32mfeature_repo_driver_hourly_stats\u001b[0m\n",
             "\n"
           ]
         }
+      ],
+      "source": [
+        "!feast apply"
       ]
     },
     {
@@ -633,13 +622,60 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "C6Fzia7YwBzz",
-        "outputId": "b99aedae-9c47-4b9f-acdd-cd02e2e091b7"
+        "outputId": "58c4c3dd-7a10-4f56-901d-1bb879ebbcb8"
       },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "----- Feature schema -----\n",
+            "\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 3 entries, 1080 to 359\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column                              Non-Null Count  Dtype              \n",
+            "---  ------                              --------------  -----              \n",
+            " 0   driver_id                           3 non-null      int64              \n",
+            " 1   label_driver_reported_satisfaction  3 non-null      int64              \n",
+            " 2   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
+            " 3   conv_rate                           3 non-null      float32            \n",
+            " 4   acc_rate                            3 non-null      float32            \n",
+            " 5   avg_daily_trips                     3 non-null      int32              \n",
+            "dtypes: datetime64[ns, UTC](1), float32(2), int32(1), int64(2)\n",
+            "memory usage: 132.0 bytes\n",
+            "None\n",
+            "\n",
+            "----- Example features -----\n",
+            "\n",
+            "      driver_id  label_driver_reported_satisfaction  \\\n",
+            "1080       1003                                   3   \n",
+            "720        1002                                   5   \n",
+            "359        1001                                   1   \n",
+            "\n",
+            "                      event_timestamp  conv_rate  acc_rate  avg_daily_trips  \n",
+            "1080 2022-06-14 17:48:10.734341+00:00   0.525623  0.217880              488  \n",
+            "720  2022-06-14 18:25:10.734338+00:00   0.181652  0.659991              974  \n",
+            "359  2022-06-14 18:50:10.734322+00:00   0.651631  0.855919              685  \n"
+          ]
+        }
+      ],
       "source": [
         "from datetime import datetime, timedelta\n",
         "import pandas as pd\n",
@@ -676,43 +712,6 @@
         "print()\n",
         "print(\"----- Example features -----\\n\")\n",
         "print(training_df.head())"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "----- Feature schema -----\n",
-            "\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 3 entries, 720 to 1081\n",
-            "Data columns (total 6 columns):\n",
-            " #   Column                              Non-Null Count  Dtype              \n",
-            "---  ------                              --------------  -----              \n",
-            " 0   driver_id                           3 non-null      int64              \n",
-            " 1   label_driver_reported_satisfaction  3 non-null      int64              \n",
-            " 2   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
-            " 3   conv_rate                           3 non-null      float32            \n",
-            " 4   acc_rate                            3 non-null      float32            \n",
-            " 5   avg_daily_trips                     3 non-null      int32              \n",
-            "dtypes: datetime64[ns, UTC](1), float32(2), int32(1), int64(2)\n",
-            "memory usage: 132.0 bytes\n",
-            "None\n",
-            "\n",
-            "----- Example features -----\n",
-            "\n",
-            "      driver_id  label_driver_reported_satisfaction  \\\n",
-            "720        1002                                   5   \n",
-            "359        1001                                   1   \n",
-            "1081       1003                                   3   \n",
-            "\n",
-            "                      event_timestamp  conv_rate  acc_rate  avg_daily_trips  \n",
-            "720  2022-04-15 13:58:30.900257+00:00   0.368052  0.417923              346  \n",
-            "359  2022-04-15 14:23:30.900240+00:00   0.003188  0.535501              593  \n",
-            "1081 2022-04-15 13:21:30.900260+00:00   0.214944  0.788695              904  \n"
-          ]
-        }
       ]
     },
     {
@@ -732,34 +731,45 @@
       "source": [
         "### Step 5a: Using `feast materialize-incremental`\n",
         "\n",
-        "We now serialize the latest values of features since the beginning of time to prepare for serving (note: `materialize-incremental` serializes all new features since the last `materialize` call)."
+        "We now serialize the latest values of features since the beginning of time to prepare for serving (note: `materialize-incremental` serializes all new features since the last `materialize` call).\n",
+        "\n",
+        "An alternative to using the CLI command is to use Python:\n",
+        "\n",
+        "```python\n",
+        "from datetime import datetime\n",
+        "store.materialize_incremental(datetime.datetime.now())\n",
+        "```"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
-        "id": "7Z6QxIebAhK5",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "d38a0e0a-2802-4408-ab92-a26baf82752e"
+        "id": "7Z6QxIebAhK5",
+        "outputId": "9b54777d-2dd8-4ec3-b4e7-e3275800a980"
       },
-      "source": [
-        "from datetime import datetime\n",
-        "!feast materialize-incremental {datetime.now().isoformat()}"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
+            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2022-06-14 19:01:13+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
             "\n",
-            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2022-04-14 14:34:38+00:00\u001b[0m to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m:\n",
-            "100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 434.17it/s]\n"
+            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2022-06-13 19:01:14+00:00\u001b[0m to \u001b[1m\u001b[32m2022-06-14 19:01:13+00:00\u001b[0m:\n",
+            "100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 403.44it/s]\n"
           ]
         }
+      ],
+      "source": [
+        "from datetime import datetime\n",
+        "!feast materialize-incremental {datetime.now().isoformat()}"
       ]
     },
     {
@@ -775,13 +785,27 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "aVIgSYhI4cvR",
-        "outputId": "eada224a-bd86-4933-f0ce-5e66870918cc"
+        "outputId": "3c60f99c-2471-4343-83ed-cc60a6a9c3b2"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--- Data directory ---\n",
+            "driver_stats.parquet  online_store.db  registry.db\n",
+            "\n",
+            "--- Schema of online store ---\n",
+            "['entity_key', 'feature_name', 'value', 'event_ts', 'created_ts']\n"
+          ]
+        }
+      ],
       "source": [
         "print(\"--- Data directory ---\")\n",
         "!ls data\n",
@@ -794,20 +818,6 @@
         "    pd.read_sql_query(\n",
         "        \"SELECT * FROM feature_repo_driver_hourly_stats\", con).columns.tolist())\n",
         "con.close()"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--- Data directory ---\n",
-            "driver_stats.parquet  online_store.db  registry.db\n",
-            "\n",
-            "--- Schema of online store ---\n",
-            "['entity_key', 'feature_name', 'value', 'event_ts', 'created_ts']\n"
-          ]
-        }
       ]
     },
     {
@@ -828,7 +838,7 @@
         "id": "GNecKOaI0J2Z"
       },
       "source": [
-        "## Step 6: Fetching feature vectors for inference\n"
+        "## Step 6: Fetching feature vectors for inference"
       ]
     },
     {
@@ -842,13 +852,26 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "a-PUsUWUxoH9",
-        "outputId": "7acecf34-8e5a-4a87-caa8-c2bff39d1376"
+        "outputId": "fc52dc04-db87-4f48-df36-d3941d485600"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'acc_rate': [0.11573542654514313, 0.19155333936214447],\n",
+            " 'avg_daily_trips': [814, 64],\n",
+            " 'conv_rate': [0.5311259031295776, 0.9771925806999207],\n",
+            " 'driver_id': [1004, 1005]}\n"
+          ]
+        }
+      ],
       "source": [
         "from pprint import pprint\n",
         "from feast import FeatureStore\n",
@@ -868,19 +891,101 @@
         ").to_dict()\n",
         "\n",
         "pprint(feature_vector)"
-      ],
-      "execution_count": 10,
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SRY87OMBoK_Z"
+      },
+      "source": [
+        "### Fetching features using feature services\n",
+        "You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found [here](https://docs.feast.dev/getting-started/concepts/feature-retrieval).\n",
+        "\n",
+        " The `driver_activity` feature service pulls all features from the `driver_hourly_stats` feature view:\n",
+        "\n",
+        "```python\n",
+        "driver_stats_fs = FeatureService(\n",
+        "    name=\"driver_activity\", features=[driver_hourly_stats_view]\n",
+        ")\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BrnAEKlPn9s8",
+        "outputId": "45f7f075-5243-4fa7-dbd4-63c0c22a68cd"
+      },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "{'acc_rate': [0.29913440346717834, 0.4562472701072693],\n",
-            " 'avg_daily_trips': [474, 261],\n",
-            " 'conv_rate': [0.20237785577774048, 0.30597227811813354],\n",
+            "{'acc_rate': [0.11573542654514313, 0.19155333936214447],\n",
+            " 'avg_daily_trips': [814, 64],\n",
+            " 'conv_rate': [0.5311259031295776, 0.9771925806999207],\n",
             " 'driver_id': [1004, 1005]}\n"
           ]
         }
+      ],
+      "source": [
+        "from feast import FeatureStore\n",
+        "feature_store = FeatureStore('.')  # Initialize the feature store\n",
+        "\n",
+        "feature_service = feature_store.get_feature_service(\"driver_activity\")\n",
+        "feature_vector = feature_store.get_online_features(\n",
+        "    features=feature_service,\n",
+        "    entity_rows=[\n",
+        "        # {join_key: entity_value}\n",
+        "        {\"driver_id\": 1004},\n",
+        "        {\"driver_id\": 1005},\n",
+        "    ],\n",
+        ").to_dict()\n",
+        "pprint(feature_vector)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_dBcqkaCnOYv"
+      },
+      "source": [
+        "## Step 7: Explore registered features with the Web UI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 52
+        },
+        "id": "mCUPypyhl5TH",
+        "outputId": "fb2475c3-b254-42e6-b638-7982d52d2a19"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nohup: appending output to 'nohup.out'\n",
+            "Open the Web UI at https://c6cuffvc4qm-496ff2e9c6d22116-8888-colab.googleusercontent.com/\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab.output import eval_js\n",
+        "host = eval_js(\"google.colab.kernel.proxyPort(8888)\")\n",
+        "\n",
+        "!nohup feast ui &\n",
+        "\n",
+        "print(f\"Open the Web UI at {host}\")"
       ]
     },
     {
@@ -897,5 +1002,21 @@
         "- Join other Feast users and contributors in [Slack](https://slack.feast.dev/) and become part of the community!"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Feast Codelab",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}