diff --git a/examples/basic/basic.ipynb b/examples/basic/basic.ipynb
index beedd5ab8a..28bc456ef9 100644
--- a/examples/basic/basic.ipynb
+++ b/examples/basic/basic.ipynb
@@ -148,48 +148,48 @@
      "output_type": "stream",
      "text": [
       "                    datetime  customer_id  daily_transactions  \\\n",
-      "0  2020-06-09 00:00:00+00:00         1001            0.564751   \n",
-      "1  2020-06-09 00:00:00+00:00         1002            3.945566   \n",
-      "2  2020-06-09 00:00:00+00:00         1003            7.291928   \n",
-      "3  2020-06-09 00:00:00+00:00         1004            6.690477   \n",
-      "4  2020-06-09 00:00:00+00:00         1005            6.415899   \n",
-      "5  2020-06-10 00:00:00+00:00         1001            0.347294   \n",
-      "6  2020-06-10 00:00:00+00:00         1002            5.363853   \n",
-      "7  2020-06-10 00:00:00+00:00         1003            0.538129   \n",
-      "8  2020-06-10 00:00:00+00:00         1004            4.755425   \n",
-      "9  2020-06-10 00:00:00+00:00         1005            2.867527   \n",
-      "10 2020-06-11 00:00:00+00:00         1001            9.493098   \n",
-      "11 2020-06-11 00:00:00+00:00         1002            5.130665   \n",
-      "12 2020-06-11 00:00:00+00:00         1003            1.794191   \n",
-      "13 2020-06-11 00:00:00+00:00         1004            4.698504   \n",
-      "14 2020-06-11 00:00:00+00:00         1005            2.908603   \n",
-      "15 2020-06-12 00:00:00+00:00         1001            9.857894   \n",
-      "16 2020-06-12 00:00:00+00:00         1002            5.416553   \n",
-      "17 2020-06-12 00:00:00+00:00         1003            5.374058   \n",
-      "18 2020-06-12 00:00:00+00:00         1004            9.834441   \n",
-      "19 2020-06-12 00:00:00+00:00         1005            0.480373   \n",
+      "0  2020-06-17 00:00:00+00:00         1001            4.900417   \n",
+      "1  2020-06-17 00:00:00+00:00         1002            7.440329   \n",
+      "2  2020-06-17 00:00:00+00:00         1003            4.224760   \n",
+      "3  2020-06-17 00:00:00+00:00         1004            5.482722   \n",
+      "4  2020-06-17 00:00:00+00:00         1005            2.200896   \n",
+      "5  2020-06-18 00:00:00+00:00         1001            8.173628   \n",
+      "6  2020-06-18 00:00:00+00:00         1002            3.164327   \n",
+      "7  2020-06-18 00:00:00+00:00         1003            7.248387   \n",
+      "8  2020-06-18 00:00:00+00:00         1004            9.274397   \n",
+      "9  2020-06-18 00:00:00+00:00         1005            7.846449   \n",
+      "10 2020-06-19 00:00:00+00:00         1001            9.028874   \n",
+      "11 2020-06-19 00:00:00+00:00         1002            5.140390   \n",
+      "12 2020-06-19 00:00:00+00:00         1003            4.537877   \n",
+      "13 2020-06-19 00:00:00+00:00         1004            6.797491   \n",
+      "14 2020-06-19 00:00:00+00:00         1005            8.234574   \n",
+      "15 2020-06-20 00:00:00+00:00         1001            8.319164   \n",
+      "16 2020-06-20 00:00:00+00:00         1002            7.158817   \n",
+      "17 2020-06-20 00:00:00+00:00         1003            4.920308   \n",
+      "18 2020-06-20 00:00:00+00:00         1004            7.974404   \n",
+      "19 2020-06-20 00:00:00+00:00         1005            2.298012   \n",
       "\n",
       "    total_transactions  \n",
-      "0                   73  \n",
-      "1                   75  \n",
-      "2                   95  \n",
-      "3                   50  \n",
-      "4                   65  \n",
-      "5                   28  \n",
-      "6                   76  \n",
-      "7                   42  \n",
+      "0                   45  \n",
+      "1                   77  \n",
+      "2                    8  \n",
+      "3                   40  \n",
+      "4                   53  \n",
+      "5                   33  \n",
+      "6                   93  \n",
+      "7                   68  \n",
       "8                   53  \n",
-      "9                   61  \n",
-      "10                  86  \n",
-      "11                  31  \n",
-      "12                  69  \n",
-      "13                   9  \n",
-      "14                  51  \n",
-      "15                  23  \n",
-      "16                   1  \n",
-      "17                  34  \n",
-      "18                  13  \n",
-      "19                  50  \n"
+      "9                   11  \n",
+      "10                  19  \n",
+      "11                   2  \n",
+      "12                   1  \n",
+      "13                  59  \n",
+      "14                  95  \n",
+      "15                  37  \n",
+      "16                  93  \n",
+      "17                  73  \n",
+      "18                  46  \n",
+      "19                  12  \n"
      ]
     }
    ],
@@ -282,7 +282,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Feature set updated: \"customer_transactions\"\n"
+      "Feature set created: \"customer_transactions\"\n"
      ]
     }
    ],
@@ -310,27 +310,26 @@
       "    ],\n",
       "    \"features\": [\n",
       "      {\n",
-      "        \"name\": \"total_transactions\",\n",
-      "        \"valueType\": \"INT64\"\n",
-      "      },\n",
-      "      {\n",
       "        \"name\": \"daily_transactions\",\n",
       "        \"valueType\": \"DOUBLE\"\n",
+      "      },\n",
+      "      {\n",
+      "        \"name\": \"total_transactions\",\n",
+      "        \"valueType\": \"INT64\"\n",
       "      }\n",
       "    ],\n",
-      "    \"maxAge\": \"0s\",\n",
       "    \"source\": {\n",
       "      \"type\": \"KAFKA\",\n",
       "      \"kafkaSourceConfig\": {\n",
-      "        \"bootstrapServers\": \"kafka:9092,localhost:9094\",\n",
+      "        \"bootstrapServers\": \"localhost:9094\",\n",
       "        \"topic\": \"feast-features\"\n",
       "      }\n",
       "    },\n",
       "    \"project\": \"default\"\n",
       "  },\n",
       "  \"meta\": {\n",
-      "    \"createdTimestamp\": \"2020-06-18T12:04:08Z\",\n",
-      "    \"status\": \"STATUS_READY\"\n",
+      "    \"createdTimestamp\": \"2020-06-26T12:27:17Z\",\n",
+      "    \"status\": \"STATUS_PENDING\"\n",
       "  }\n",
       "}\n"
      ]
@@ -357,17 +356,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/50 [00:00<?, ?rows/s]"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -379,7 +370,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 50/50 [00:01<00:00, 48.83rows/s]"
+      "100%|██████████| 50/50 [00:01<00:00, 47.23rows/s]"
      ]
     },
     {
@@ -403,10 +394,10 @@
     {
      "data": {
       "text/plain": [
-       "'8da648b7-d6ac-3970-90c6-cf789078c869'"
+       "'5e650050-f41d-39fc-bc56-d602c4a478d2'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,34 +422,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "field_values {\n",
-      "  fields {\n",
-      "    key: \"customer_id\"\n",
-      "    value {\n",
-      "      int64_val: 1001\n",
-      "    }\n",
+      "[fields {\n",
+      "  key: \"customer_id\"\n",
+      "  value {\n",
+      "    int64_val: 1001\n",
       "  }\n",
-      "  fields {\n",
-      "    key: \"daily_transactions\"\n",
-      "    value {\n",
-      "      double_val: 5.1228456657485495\n",
-      "    }\n",
+      "}\n",
+      "fields {\n",
+      "  key: \"daily_transactions\"\n",
+      "  value {\n",
+      "    double_val: 0.12021977894872915\n",
       "  }\n",
-      "  fields {\n",
-      "    key: \"total_transactions\"\n",
-      "    value {\n",
-      "      int64_val: 96\n",
-      "    }\n",
+      "}\n",
+      "fields {\n",
+      "  key: \"total_transactions\"\n",
+      "  value {\n",
+      "    int64_val: 0\n",
       "  }\n",
       "}\n",
-      "\n"
+      "statuses {\n",
+      "  key: \"customer_id\"\n",
+      "  value: PRESENT\n",
+      "}\n",
+      "statuses {\n",
+      "  key: \"daily_transactions\"\n",
+      "  value: PRESENT\n",
+      "}\n",
+      "statuses {\n",
+      "  key: \"total_transactions\"\n",
+      "  value: PRESENT\n",
+      "}\n",
+      "]\n"
      ]
     }
    ],
@@ -469,15 +470,41 @@
     "        f\"total_transactions\",\n",
     "    ],\n",
     "    entity_rows=[\n",
-    "        GetOnlineFeaturesRequest.EntityRow(\n",
-    "            fields={\n",
-    "                \"customer_id\": Value(\n",
-    "                    int64_val=1001)\n",
-    "            }\n",
-    "        )\n",
+    "        {\n",
+    "            \"customer_id\": 1001\n",
+    "        }\n",
     "    ],\n",
     ")\n",
-    "print(online_features)"
+    "print(online_features.field_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To retrieve the fields in dictionary format, we can utilize `to_dict()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'daily_transactions': [0.12021977894872915],\n",
+       " 'total_transactions': [0],\n",
+       " 'customer_id': [1001]}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "online_features.to_dict()"
    ]
   },
   {
@@ -708,9 +735,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "local-feast",
    "language": "python",
-   "name": "python3"
+   "name": "local-feast"
   },
   "language_info": {
    "codemirror_mode": {
@@ -726,5 +753,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/feast-xgboost-churn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb b/examples/feast-xgboost-churn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb
index 2554c1eaee..f7adc80d8d 100644
--- a/examples/feast-xgboost-churn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb	
+++ b/examples/feast-xgboost-churn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb	
@@ -712,7 +712,7 @@
        "      <th>customer_id</th>\n",
        "      <td>7032</td>\n",
        "      <td>7032</td>\n",
-       "      <td>0256-LTHVJ</td>\n",
+       "      <td>1934-SJVJK</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1204,7 +1204,7 @@
       ],
       "text/plain": [
        "                                      count unique         top freq  \\\n",
-       "customer_id                            7032   7032  0256-LTHVJ    1   \n",
+       "customer_id                            7032   7032  1934-SJVJK    1   \n",
        "gender                                 7032    NaN         NaN  NaN   \n",
        "seniorcitizen                          7032    NaN         NaN  NaN   \n",
        "partner                                7032    NaN         NaN  NaN   \n",
@@ -1562,7 +1562,7 @@
            -0.007514979909200033,
            -0.01632782307070617,
            -0.013092839264555001,
-           -8.067457759124324E-4,
+           -0.0008067457759124324,
            -0.008507162405232782,
            -0.0071243969867245535,
            -0.010105418366566195,
@@ -1579,16 +1579,16 @@
            -0.003603167413572989,
            -0.015973079031173835,
            0.001631872518613598,
-           8.437084888753327E-4,
+           0.0008437084888753327,
            0.01319936726545174,
            -0.0010503798619876774,
-           -6.494991207074467E-4,
+           -0.0006494991207074467,
            -0.010516394125351592,
            -0.004318975744551275,
            0.01627881894278637,
            0.005285371870295646,
            -0.013779327268354416,
-           4.783950839776602E-5
+           4.783950839776602e-05
           ],
           [
            -0.001819390613419179,
@@ -1645,7 +1645,7 @@
            0.1425612874681736,
            -0.0010430787434336079,
            0.0012346095228208073,
-           -2.855204740384597E-4,
+           -0.0002855204740384597,
            -0.2802019157901561,
            0.08306706395255747,
            0.24733370647615796,
@@ -1795,7 +1795,7 @@
            0.11139068731904943,
            0.0869415675760231,
            0.09045518641091457,
-           -3.6426636786500763E-4,
+           -0.00036426636786500763,
            -0.17407470231312427,
            -0.26736609150996915,
            -0.08408097138202993,
@@ -1807,7 +1807,7 @@
            0.510100290145439
           ],
           [
-           -8.067457759124324E-4,
+           -0.0008067457759124324,
            0.059513871482029225,
            0.1535564364182745,
            0.013899668260943368,
@@ -2159,8 +2159,8 @@
            0.04275388869901973,
            -0.0014704079413737543,
            -0.013428683581908657,
-           -4.678565419111244E-4,
-           -7.9001062411148E-4,
+           -0.0004678565419111244,
+           -0.00079001062411148,
            0.014665913237832425,
            0.013786269825793156,
            -0.16136793538251534,
@@ -2205,7 +2205,7 @@
           [
            0.004744965758849955,
            -0.18251949495535458,
-           -2.855204740384597E-4,
+           -0.0002855204740384597,
            0.13838288994798562,
            0.1718171065699321,
            -0.33279949932167546,
@@ -2300,7 +2300,7 @@
            0.05762874929825631,
            0.06758968145792751,
            -0.10954646682258033,
-           1.971241890499786E-4,
+           0.0001971241890499786,
            -0.2512993032332009,
            -0.017196458507178478,
            0.15389327309228798,
@@ -2419,13 +2419,13 @@
            0.1826633671546002
           ],
           [
-           8.437084888753327E-4,
+           0.0008437084888753327,
            0.17132216591713703,
            -0.08320661736633733,
            -0.1492739811934336,
            0.0027471183312986857,
            -0.11229466175861408,
-           -3.6426636786500763E-4,
+           -0.00036426636786500763,
            -0.003308493511411254,
            -0.11480726996437085,
            0.1447470086556032,
@@ -2475,7 +2475,7 @@
            -0.3059839224841771,
            0.3196937439459745,
            0.006208692442055045,
-           1.971241890499786E-4,
+           0.0001971241890499786,
            -0.007422540118618717,
            -0.28809669563791657,
            -0.28558254792863036,
@@ -2527,7 +2527,7 @@
            -0.5924430690900127
           ],
           [
-           -6.494991207074467E-4,
+           -0.0006494991207074467,
            0.0018604411671567017,
            -0.048481275955609554,
            -0.0014594010172321779,
@@ -2579,7 +2579,7 @@
            -0.03215707580783382,
            0.014777815951004133,
            0.023690157563696343,
-           -4.678565419111244E-4,
+           -0.0004678565419111244,
            0.005613761063888361,
            -0.006230482005525746,
            -0.05215631834144274,
@@ -2615,7 +2615,7 @@
            -0.08063023581160907,
            0.009750281447949528,
            0.07573985650934924,
-           -7.9001062411148E-4,
+           -0.00079001062411148,
            0.01746632456448475,
            -0.020153136101165186,
            -0.19870886170688193,
@@ -2743,7 +2743,7 @@
            0.6510648032262032
           ],
           [
-           4.783950839776602E-5,
+           4.783950839776602e-05,
            0.10241060539532633,
            0.31907236323857324,
            0.0646532494217739,
@@ -3615,20 +3615,20 @@
        "<div>\n",
        "        \n",
        "        \n",
-       "            <div id=\"8615889c-7fc0-42d3-b0b9-bf4071051c46\" class=\"plotly-graph-div\" style=\"height:720px; width:800px;\"></div>\n",
+       "            <div id=\"3a217169-1df5-45e7-a39c-b4760eaa98cd\" class=\"plotly-graph-div\" style=\"height:720px; width:800px;\"></div>\n",
        "            <script type=\"text/javascript\">\n",
        "                require([\"plotly\"], function(Plotly) {\n",
        "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
        "                    \n",
-       "                if (document.getElementById(\"8615889c-7fc0-42d3-b0b9-bf4071051c46\")) {\n",
+       "                if (document.getElementById(\"3a217169-1df5-45e7-a39c-b4760eaa98cd\")) {\n",
        "                    Plotly.newPlot(\n",
-       "                        '8615889c-7fc0-42d3-b0b9-bf4071051c46',\n",
+       "                        '3a217169-1df5-45e7-a39c-b4760eaa98cd',\n",
        "                        [{\"colorbar\": {\"title\": {\"side\": \"right\", \"text\": \"Pearson Correlation coefficient\"}}, \"colorscale\": [[0.0, \"#440154\"], [0.1111111111111111, \"#482878\"], [0.2222222222222222, \"#3e4989\"], [0.3333333333333333, \"#31688e\"], [0.4444444444444444, \"#26828e\"], [0.5555555555555556, \"#1f9e89\"], [0.6666666666666666, \"#35b779\"], [0.7777777777777778, \"#6ece58\"], [0.8888888888888888, \"#b5de2b\"], [1.0, \"#fde725\"]], \"type\": \"heatmap\", \"x\": [\"gender\", \"seniorcitizen\", \"partner\", \"dependents\", \"phoneservice\", \"onlinesecurity\", \"onlinebackup\", \"deviceprotection\", \"techsupport\", \"streamingtv\", \"streamingmovies\", \"paperlessbilling\", \"churn\", \"multiplelines_no\", \"multiplelines_no_phone_service\", \"multiplelines_yes\", \"internetservice_dsl\", \"internetservice_fiber_optic\", \"internetservice_no\", \"contract_month_to_month\", \"contract_one_year\", \"contract_two_year\", 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        "                        {\"responsive\": true}\n",
        "                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('8615889c-7fc0-42d3-b0b9-bf4071051c46');\n",
+       "var gd = document.getElementById('3a217169-1df5-45e7-a39c-b4760eaa98cd');\n",
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@@ -3701,7 +3701,18 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/user/venv/local-feast/lib/python3.7/site-packages/patsy/constraint.py:13: DeprecationWarning:\n",
+      "\n",
+      "Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
@@ -3740,7 +3751,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[04:09:49] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:08] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -3810,9 +3821,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/conda/lib/python3.7/site-packages/plotly/tools.py:465: DeprecationWarning:\n",
+      "/Users/user/venv/local-feast/lib/python3.7/site-packages/plotly/tools.py:465: DeprecationWarning:\n",
       "\n",
       "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
+      "\n",
+      "/Users/user/venv/local-feast/lib/python3.7/site-packages/plotly/basedatatypes.py:1823: DeprecationWarning:\n",
+      "\n",
+      "The append_trace method is deprecated and will be removed in a future version.\n",
+      "Please use the add_trace method with the row and col parameters.\n",
+      "\n",
       "\n"
      ]
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+          0.012016979046165943,
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-       "            <div id=\"bb3ae0ac-5712-4829-a90d-6802b212144c\" class=\"plotly-graph-div\" style=\"height:900px; width:800px;\"></div>\n",
+       "            <div id=\"d7a2a79d-9bae-4e33-8cb0-389aa7d875c4\" class=\"plotly-graph-div\" style=\"height:900px; width:800px;\"></div>\n",
        "            <script type=\"text/javascript\">\n",
        "                require([\"plotly\"], function(Plotly) {\n",
        "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
        "                    \n",
-       "                if (document.getElementById(\"bb3ae0ac-5712-4829-a90d-6802b212144c\")) {\n",
+       "                if (document.getElementById(\"d7a2a79d-9bae-4e33-8cb0-389aa7d875c4\")) {\n",
        "                    Plotly.newPlot(\n",
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@@ -6197,7 +6214,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[04:09:51] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:10] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6205,7 +6222,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:52] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:12] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6213,7 +6230,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:53] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:13] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6221,7 +6238,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:55] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:14] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6229,7 +6246,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:56] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:16] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6237,7 +6254,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:57] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:17] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6245,7 +6262,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:09:59] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:18] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6253,7 +6270,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:00] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:19] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6261,7 +6278,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:01] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:20] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6269,7 +6286,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:03] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:21] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6277,7 +6294,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:04] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:23] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6285,7 +6302,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:06] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:24] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6293,7 +6310,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:07] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:25] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6301,7 +6318,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:09] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:26] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6309,7 +6326,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:10] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:28] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6317,7 +6334,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:12] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:29] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6325,7 +6342,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:13] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:30] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6333,7 +6350,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:14] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:32] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6341,7 +6358,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:16] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:33] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6349,7 +6366,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:17] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:34] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6357,7 +6374,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:19] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:36] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6365,7 +6382,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:20] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:37] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6373,7 +6390,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:21] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:39] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6381,7 +6398,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:22] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:40] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6389,21 +6406,15 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:24] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:41] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
       "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[04:10:25] WARNING: /workspace/src/learner.cc:480: \n",
+      "\n",
+      "[20:47:43] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6411,7 +6422,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:26] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:44] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6419,7 +6430,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:28] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:46] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6427,7 +6438,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:29] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:47] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6435,7 +6446,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:30] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:48] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6443,7 +6454,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:31] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:49] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6451,7 +6462,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:33] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:51] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6459,7 +6470,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:34] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:52] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6467,7 +6478,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:35] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:53] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6475,7 +6486,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:37] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:54] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6483,7 +6494,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:38] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:55] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6491,7 +6502,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:39] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:57] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6499,7 +6510,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:40] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:58] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6507,7 +6518,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:42] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:47:59] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6515,7 +6526,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:43] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:01] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6523,7 +6534,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:44] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:02] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6531,7 +6542,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:45] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:03] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6539,7 +6550,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:47] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:05] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6547,7 +6558,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:48] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:06] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6555,7 +6566,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:49] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:07] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6563,7 +6574,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:51] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:08] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6571,7 +6582,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:52] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:09] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6579,7 +6590,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:53] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:10] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6587,7 +6598,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:54] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:12] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6595,7 +6606,7 @@
       "  verification. Please open an issue if you find above cases.\n",
       "\n",
       "\n",
-      "[04:10:56] WARNING: /workspace/src/learner.cc:480: \n",
+      "[20:48:13] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
       "Parameters: { silent } might not be used.\n",
       "\n",
       "  This may not be accurate due to some parameters are only used in language bindings but\n",
@@ -6605,9 +6616,19 @@
       "\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/user/venv/local-feast/lib/python3.7/site-packages/yellowbrick/base.py:259: DeprecationWarning:\n",
+      "\n",
+      "this method is deprecated, please use show() instead\n",
+      "\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x396 with 1 Axes>"
       ]
@@ -6618,7 +6639,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fecc3b82690>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12d23b510>"
       ]
      },
      "execution_count": 10,
@@ -6859,9 +6880,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature set created: \"customer_churn\"\n"
+     ]
+    }
+   ],
    "source": [
     "client.apply(customer_churn_fs)"
    ]
@@ -6893,71 +6922,75 @@
       "    ],\n",
       "    \"features\": [\n",
       "      {\n",
-      "        \"name\": \"contract_one_year\",\n",
+      "        \"name\": \"tenure\",\n",
+      "        \"valueType\": \"DOUBLE\"\n",
+      "      },\n",
+      "      {\n",
+      "        \"name\": \"churn\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure\",\n",
+      "        \"name\": \"monthlycharges\",\n",
       "        \"valueType\": \"DOUBLE\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure_group_tenure_12_24\",\n",
+      "        \"name\": \"paymentmethod_credit_card_automatic\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"contract_two_year\",\n",
+      "        \"name\": \"paymentmethod_electronic_check\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"internetservice_no\",\n",
+      "        \"name\": \"dependents\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure_group_tenure_24_48\",\n",
+      "        \"name\": \"internetservice_no\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"techsupport\",\n",
+      "        \"name\": \"onlinesecurity\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"phoneservice\",\n",
+      "        \"name\": \"streamingtv\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"internetservice_fiber_optic\",\n",
+      "        \"name\": \"multiplelines_no_phone_service\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"paymentmethod_electronic_check\",\n",
+      "        \"name\": \"contract_two_year\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"multiplelines_no\",\n",
-      "        \"valueType\": \"INT64\"\n",
+      "        \"name\": \"totalcharges\",\n",
+      "        \"valueType\": \"DOUBLE\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"onlinesecurity\",\n",
+      "        \"name\": \"streamingmovies\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"multiplelines_no_phone_service\",\n",
+      "        \"name\": \"internetservice_dsl\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"streamingtv\",\n",
+      "        \"name\": \"contract_one_year\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"multiplelines_yes\",\n",
+      "        \"name\": \"tenure_group_tenure_24_48\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure_group_tenure_0_12\",\n",
+      "        \"name\": \"multiplelines_no\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"paymentmethod_credit_card_automatic\",\n",
+      "        \"name\": \"techsupport\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
@@ -6965,35 +6998,35 @@
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure_group_tenure_48_60\",\n",
+      "        \"name\": \"phoneservice\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"gender\",\n",
+      "        \"name\": \"tenure_group_tenure_12_24\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"deviceprotection\",\n",
+      "        \"name\": \"gender\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"paymentmethod_bank_transfer_automatic\",\n",
+      "        \"name\": \"partner\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"seniorcitizen\",\n",
+      "        \"name\": \"paymentmethod_mailed_check\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"totalcharges\",\n",
-      "        \"valueType\": \"DOUBLE\"\n",
+      "        \"name\": \"contract_month_to_month\",\n",
+      "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"internetservice_dsl\",\n",
+      "        \"name\": \"tenure_group_tenure_48_60\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"churn\",\n",
+      "        \"name\": \"tenure_group_tenure_0_12\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
@@ -7001,46 +7034,42 @@
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"streamingmovies\",\n",
+      "        \"name\": \"seniorcitizen\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"monthlycharges\",\n",
-      "        \"valueType\": \"DOUBLE\"\n",
-      "      },\n",
-      "      {\n",
-      "        \"name\": \"dependents\",\n",
+      "        \"name\": \"multiplelines_yes\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"contract_month_to_month\",\n",
+      "        \"name\": \"deviceprotection\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"tenure_group_tenure_gt_60\",\n",
+      "        \"name\": \"paymentmethod_bank_transfer_automatic\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"partner\",\n",
+      "        \"name\": \"internetservice_fiber_optic\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      },\n",
       "      {\n",
-      "        \"name\": \"paymentmethod_mailed_check\",\n",
+      "        \"name\": \"tenure_group_tenure_gt_60\",\n",
       "        \"valueType\": \"INT64\"\n",
       "      }\n",
       "    ],\n",
       "    \"source\": {\n",
       "      \"type\": \"KAFKA\",\n",
       "      \"kafkaSourceConfig\": {\n",
-      "        \"bootstrapServers\": \"kafka:9092,localhost:9094\",\n",
+      "        \"bootstrapServers\": \"localhost:9094\",\n",
       "        \"topic\": \"feast-features\"\n",
       "      }\n",
       "    },\n",
       "    \"project\": \"default\"\n",
       "  },\n",
       "  \"meta\": {\n",
-      "    \"createdTimestamp\": \"2020-06-19T02:57:41Z\",\n",
-      "    \"status\": \"STATUS_READY\"\n",
+      "    \"createdTimestamp\": \"2020-06-26T12:48:29Z\",\n",
+      "    \"status\": \"STATUS_PENDING\"\n",
       "  }\n",
       "}\n"
      ]
@@ -7070,14 +7099,6 @@
    "execution_count": 18,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/7032 [00:00<?, ?rows/s]"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -7089,7 +7110,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 7032/7032 [00:01<00:00, 6229.68rows/s]"
+      "100%|██████████| 7032/7032 [00:04<00:00, 1735.70rows/s]"
      ]
     },
     {
@@ -7113,7 +7134,7 @@
     {
      "data": {
       "text/plain": [
-       "'793dbdd7-160c-3f4a-b80a-629ec2db31fc'"
+       "'ca7c1deb-4cdd-3989-8173-3a5c1f37adfb'"
       ]
      },
      "execution_count": 18,
@@ -7219,7 +7240,7 @@
     "        entity_rows = []\n",
     "        for customer_id in customer_ids:\n",
     "            entity_rows.append(\n",
-    "                GetOnlineFeaturesRequest.EntityRow(fields={'customer_id': Value(string_val=customer_id)})\n",
+    "                {'customer_id': customer_id}\n",
     "            )\n",
     "\n",
     "        # Retrieve online features from Feast for given entities\n",
@@ -7349,9 +7370,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -7465,9 +7484,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "local-feast",
    "language": "python",
-   "name": "python3"
+   "name": "local-feast"
   },
   "language_info": {
    "codemirror_mode": {
@@ -7484,4 +7503,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/sdk/python/feast/client.py b/sdk/python/feast/client.py
index 4dad553d40..c89c462090 100644
--- a/sdk/python/feast/client.py
+++ b/sdk/python/feast/client.py
@@ -23,7 +23,7 @@
 import warnings
 from collections import OrderedDict
 from math import ceil
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union, cast
 
 import grpc
 import pandas as pd
@@ -76,6 +76,7 @@
 from feast.loaders.abstract_producer import get_producer
 from feast.loaders.file import export_source_to_staging_location
 from feast.loaders.ingest import KAFKA_CHUNK_PRODUCTION_TIMEOUT, get_feature_row_chunks
+from feast.online_response import OnlineResponse
 from feast.serving.ServingService_pb2 import (
     DataFormat,
     DatasetSource,
@@ -85,14 +86,17 @@
     GetFeastServingInfoRequest,
     GetFeastServingInfoResponse,
     GetOnlineFeaturesRequest,
-    GetOnlineFeaturesResponse,
 )
 from feast.serving.ServingService_pb2_grpc import ServingServiceStub
+from feast.type_map import _python_value_to_proto_value, python_type_to_feast_value_type
+from feast.types.Value_pb2 import Value as Value
 
 _logger = logging.getLogger(__name__)
 
 CPU_COUNT: int = len(os.sched_getaffinity(0))
 
+warnings.simplefilter("once", DeprecationWarning)
+
 
 class Client:
     """
@@ -675,10 +679,10 @@ def get_historical_features(
     def get_online_features(
         self,
         feature_refs: List[str],
-        entity_rows: List[GetOnlineFeaturesRequest.EntityRow],
+        entity_rows: List[Union[GetOnlineFeaturesRequest.EntityRow, Dict[str, Any]]],
         project: Optional[str] = None,
         omit_entities: bool = False,
-    ) -> GetOnlineFeaturesResponse:
+    ) -> OnlineResponse:
         """
         Retrieves the latest online feature data from Feast Serving
 
@@ -688,9 +692,8 @@ def get_online_features(
                 "feature_set:feature" where "feature_set" & "feature" refer to
                 the feature and feature set names respectively.
                 Only the feature name is required.
-            entity_rows: List of GetFeaturesRequest.EntityRow where each row
-                contains entities. Timestamp should not be set for online
-                retrieval. All entity types within a feature
+            entity_rows: A list of dictionaries where each key is an entity and each value is
+                feast.types.Value or Python native form.
             project: Optionally specify the the project override. If specified, uses given project for retrieval.
                 Overrides the projects specified in Feature References if also are specified.
             omit_entities: If true will omit entity values in the returned feature data.
@@ -698,6 +701,19 @@ def get_online_features(
             GetOnlineFeaturesResponse containing the feature data in records.
             Each EntityRow provided will yield one record, which contains
             data fields with data value and field status metadata (if included).
+
+        Examples:
+            >>> from feast import Client
+            >>>
+            >>> feast_client = Client(core_url="localhost:6565", serving_url="localhost:6566")
+            >>> feature_refs = ["daily_transactions"]
+            >>> entity_rows = [{"customer_id": 0},{"customer_id": 1}]
+            >>>
+            >>> online_response = feast_client.get_online_features(
+            >>>     feature_refs, entity_rows, project="my_project")
+            >>> online_response_dict = online_response.to_dict()
+            >>> print(online_response_dict)
+            {'daily_transactions': [1.1,1.2], 'customer_id': [0,1]}
         """
 
         try:
@@ -705,13 +721,14 @@ def get_online_features(
                 GetOnlineFeaturesRequest(
                     omit_entities_in_response=omit_entities,
                     features=_build_feature_references(feature_ref_strs=feature_refs),
-                    entity_rows=entity_rows,
+                    entity_rows=_infer_online_entity_rows(entity_rows),
                     project=project if project is not None else self.project,
                 )
             )
         except grpc.RpcError as e:
             raise grpc.RpcError(e.details())
 
+        response = OnlineResponse(response)
         return response
 
     def list_ingest_jobs(
@@ -980,6 +997,58 @@ def _get_grpc_metadata(self):
         return ()
 
 
+def _infer_online_entity_rows(
+    entity_rows: List[Union[GetOnlineFeaturesRequest.EntityRow, Dict[str, Any]]],
+) -> List[GetOnlineFeaturesRequest.EntityRow]:
+    """
+    Builds a list of EntityRow protos from Python native type format passed by user.
+
+    Args:
+        entity_rows: A list of dictionaries where each key is an entity and each value is
+            feast.types.Value or Python native form.
+
+    Returns:
+        A list of EntityRow protos parsed from args.
+    """
+
+    # Maintain backward compatibility with users providing EntityRow Proto
+    if entity_rows and isinstance(entity_rows[0], GetOnlineFeaturesRequest.EntityRow):
+        warnings.warn(
+            "entity_rows parameter will only be accepting Dict format from Feast v0.7 onwards",
+            DeprecationWarning,
+        )
+        entity_rows_proto = cast(
+            List[Union[GetOnlineFeaturesRequest.EntityRow]], entity_rows
+        )
+        return entity_rows_proto
+
+    entity_rows_dicts = cast(List[Dict[str, Any]], entity_rows)
+    entity_row_list = []
+    entity_type_map = dict()
+
+    for entity in entity_rows_dicts:
+        for key, value in entity.items():
+            # Allow for feast.types.Value
+            if isinstance(value, Value):
+                proto_value = value
+            else:
+                # Infer the specific type for this row
+                current_dtype = python_type_to_feast_value_type(name=key, value=value)
+
+                if key not in entity_type_map:
+                    entity_type_map[key] = current_dtype
+                else:
+                    if current_dtype != entity_type_map[key]:
+                        raise TypeError(
+                            f"Input entity {key} has mixed types, {current_dtype} and {entity_type_map[key]}. That is not allowed. "
+                        )
+                proto_value = _python_value_to_proto_value(current_dtype, value)
+            entity_row_list.append(
+                GetOnlineFeaturesRequest.EntityRow(fields={key: proto_value})
+            )
+    return entity_row_list
+
+
 def _build_feature_references(
     feature_ref_strs: List[str], project: Optional[str] = None
 ) -> List[FeatureReference]:
diff --git a/sdk/python/feast/online_response.py b/sdk/python/feast/online_response.py
new file mode 100644
index 0000000000..a90c25e09d
--- /dev/null
+++ b/sdk/python/feast/online_response.py
@@ -0,0 +1,54 @@
+# Copyright 2020 The Feast Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List
+
+from feast.serving.ServingService_pb2 import GetOnlineFeaturesResponse
+from feast.type_map import feast_value_type_to_python_type
+
+
+class OnlineResponse:
+    """
+    Defines a online response in feast.
+    """
+
+    def __init__(self, online_response_proto: GetOnlineFeaturesResponse):
+        """
+        Construct a native online response from its protobuf version.
+
+        Args:
+        online_response_proto: GetOnlineResponse proto object to construct from.
+        """
+        self.proto = online_response_proto
+
+    @property
+    def field_values(self):
+        """
+        Getter for GetOnlineResponse's field_values.
+        """
+        return self.proto.field_values
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Converts GetOnlineFeaturesResponse features into a dictionary form.
+        """
+        fields = [k for row in self.field_values for k, _ in row.fields.items()]
+        features_dict: Dict[str, List[Any]] = {k: list() for k in fields}
+
+        for row in self.field_values:
+            for feature in features_dict.keys():
+                native_type_value = feast_value_type_to_python_type(row.fields[feature])
+                features_dict[feature].append(native_type_value)
+
+        return features_dict
diff --git a/sdk/python/feast/staging/storage_client.py b/sdk/python/feast/staging/storage_client.py
index a71a3b2b5d..3b391410b2 100644
--- a/sdk/python/feast/staging/storage_client.py
+++ b/sdk/python/feast/staging/storage_client.py
@@ -116,7 +116,7 @@ def list_files(self, bucket: str, path: str) -> List[str]:
                 if re.match(regex, file) and file not in path
             ]
         else:
-            return [f"{S3}://{bucket}/{path.lstrip('/')}"]
+            return [f"{GS}://{bucket}/{path.lstrip('/')}"]
 
     def upload_file(self, local_path: str, bucket: str, remote_path: str):
         """
diff --git a/sdk/python/feast/type_map.py b/sdk/python/feast/type_map.py
index 56757e9ef9..82ac90bbd1 100644
--- a/sdk/python/feast/type_map.py
+++ b/sdk/python/feast/type_map.py
@@ -18,6 +18,7 @@
 import numpy as np
 import pandas as pd
 import pyarrow as pa
+from google.protobuf.json_format import MessageToDict
 from google.protobuf.timestamp_pb2 import Timestamp
 from pyarrow.lib import TimestampType
 
@@ -38,6 +39,44 @@
 from feast.value_type import ValueType
 
 
+def feast_value_type_to_python_type(field_value_proto: ProtoValue) -> Any:
+    """
+    Converts field value Proto to Dict and returns each field's Feast Value Type value
+    in their respective Python value.
+
+    Args:
+        field_value_proto: Field value Proto
+
+    Returns:
+        Python native type representation/version of the given field_value_proto
+    """
+    field_value_dict = MessageToDict(field_value_proto)
+
+    for k, v in field_value_dict.items():
+        if k == "int64Val":
+            return int(v)
+        if k == "bytesVal":
+            return bytes(v)
+        if (k == "int64ListVal") or (k == "int32ListVal"):
+            return [int(item) for item in v["val"]]
+        if (k == "floatListVal") or (k == "doubleListVal"):
+            return [float(item) for item in v["val"]]
+        if k == "stringListVal":
+            return [str(item) for item in v["val"]]
+        if k == "bytesListVal":
+            return [bytes(item) for item in v["val"]]
+        if k == "boolListVal":
+            return [bool(item) for item in v["val"]]
+
+        if k in ["int32Val", "floatVal", "doubleVal", "stringVal", "boolVal"]:
+            return v
+        else:
+            raise TypeError(
+                f"Casting to Python native type for type {k} failed. "
+                f"Type {k} not found"
+            )
+
+
 def python_type_to_feast_value_type(
     name: str, value, recurse: bool = True
 ) -> ValueType:
@@ -80,7 +119,7 @@ def python_type_to_feast_value_type(
     if type_name in type_map:
         return type_map[type_name]
 
-    if type_name == "ndarray":
+    if type_name == "ndarray" or isinstance(value, list):
         if recurse:
 
             # Convert to list type
@@ -89,10 +128,15 @@ def python_type_to_feast_value_type(
             # This is the final type which we infer from the list
             common_item_value_type = None
             for item in list_items:
-                # Get the type from the current item, only one level deep
-                current_item_value_type = python_type_to_feast_value_type(
-                    name=name, value=item, recurse=False
-                )
+                if isinstance(item, ProtoValue):
+                    current_item_value_type = _proto_str_to_value_type(
+                        item.WhichOneof("val")
+                    )
+                else:
+                    # Get the type from the current item, only one level deep
+                    current_item_value_type = python_type_to_feast_value_type(
+                        name=name, value=item, recurse=False
+                    )
                 # Validate whether the type stays consistent
                 if (
                     common_item_value_type
@@ -353,6 +397,36 @@ def _python_value_to_proto_value(feast_value_type, value) -> ProtoValue:
     raise Exception(f"Unsupported data type: ${str(type(value))}")
 
 
+def _proto_str_to_value_type(proto_str: str) -> ValueType:
+    """
+    Returns Feast ValueType given Feast ValueType string.
+
+    Args:
+        proto_str: str
+
+    Returns:
+        A variant of ValueType.
+    """
+    type_map = {
+        "int32_val": ValueType.INT32,
+        "int64_val": ValueType.INT64,
+        "double_val": ValueType.DOUBLE,
+        "float_val": ValueType.FLOAT,
+        "string_val": ValueType.STRING,
+        "bytes_val": ValueType.BYTES,
+        "bool_val": ValueType.BOOL,
+        "int32_list_val": ValueType.INT32_LIST,
+        "int64_list_val": ValueType.INT64_LIST,
+        "double_list_val": ValueType.DOUBLE_LIST,
+        "float_list_val": ValueType.FLOAT_LIST,
+        "string_list_val": ValueType.STRING_LIST,
+        "bytes_list_val": ValueType.BYTES_LIST,
+        "bool_list_val": ValueType.BOOL_LIST,
+    }
+
+    return type_map[proto_str]
+
+
 def pa_to_feast_value_attr(pa_type: object):
     """
     Returns the equivalent Feast ValueType string for the given pa.lib type.
diff --git a/tests/e2e/redis/basic-ingest-redis-serving.py b/tests/e2e/redis/basic-ingest-redis-serving.py
index 66d89cb396..16b4b1f41d 100644
--- a/tests/e2e/redis/basic-ingest-redis-serving.py
+++ b/tests/e2e/redis/basic-ingest-redis-serving.py
@@ -27,6 +27,7 @@
 )
 from feast.source import KafkaSource
 from feast.type_map import ValueType
+from feast.types.Value_pb2 import Int64List
 from feast.types.Value_pb2 import Value as Value
 from feast.wait import wait_retry_backoff
 
@@ -301,6 +302,262 @@ def try_get_features():
     )
 
 
+@pytest.fixture(scope="module")
+def nonlist_entity_dataframe():
+    # Dataframe setup for feature retrieval with entity provided not in list format
+    N_ROWS = 2
+    time_offset = datetime.utcnow().replace(tzinfo=pytz.utc)
+    customer_df = pd.DataFrame(
+        {
+            "datetime": [time_offset] * N_ROWS,
+            "customer_id2": [i for i in range(N_ROWS)],
+            "customer2_rating": [i for i in range(N_ROWS)],
+            "customer2_cost": [float(i) + 0.5 for i in range(N_ROWS)],
+            "customer2_past_transactions_int": [[i, i + 2] for i in range(N_ROWS)],
+            "customer2_past_transactions_double": [
+                [float(i) + 0.5, float(i) + 2] for i in range(N_ROWS)
+            ],
+            "customer2_past_transactions_float": [
+                [float(i) + 0.5, float(i) + 2] for i in range(N_ROWS)
+            ],
+            "customer2_past_transactions_string": [
+                ["first_" + str(i), "second_" + str(i)] for i in range(N_ROWS)
+            ],
+            "customer2_past_transactions_bool": [[True, False] for _ in range(N_ROWS)],
+        }
+    )
+    return customer_df
+
+
+@pytest.fixture(scope="module")
+def list_entity_dataframe():
+    # Dataframe setup for feature retrieval with entity provided in list format
+    N_ROWS = 2
+    time_offset = datetime.utcnow().replace(tzinfo=pytz.utc)
+    customer_df = pd.DataFrame(
+        {
+            "datetime": [time_offset] * N_ROWS,
+            "district_ids": [
+                [np.int64(i), np.int64(i + 1), np.int64(i + 2)] for i in range(N_ROWS)
+            ],
+            "district_rating": [i for i in range(N_ROWS)],
+            "district_cost": [float(i) + 0.5 for i in range(N_ROWS)],
+            "district_past_transactions_int": [[i, i + 2] for i in range(N_ROWS)],
+            "district_past_transactions_double": [
+                [float(i) + 0.5, float(i) + 2] for i in range(N_ROWS)
+            ],
+            "district_past_transactions_float": [
+                [float(i) + 0.5, float(i) + 2] for i in range(N_ROWS)
+            ],
+            "district_past_transactions_string": [
+                ["first_" + str(i), "second_" + str(i)] for i in range(N_ROWS)
+            ],
+            "district_past_transactions_bool": [[True, False] for _ in range(N_ROWS)],
+        }
+    )
+    return customer_df
+
+
+@pytest.mark.timeout(600)
+@pytest.mark.run(order=14)
+def test_basic_retrieve_online_entity_nonlistform(
+    client, nonlist_entity_dataframe, list_entity_dataframe
+):
+    # Case 1: Feature retrieval with multiple entities retrieval check
+    customer_fs = FeatureSet(
+        name="customer2",
+        features=[
+            Feature(name="customer2_rating", dtype=ValueType.INT64),
+            Feature(name="customer2_cost", dtype=ValueType.FLOAT),
+            Feature(name="customer2_past_transactions_int", dtype=ValueType.INT64_LIST),
+            Feature(
+                name="customer2_past_transactions_double", dtype=ValueType.DOUBLE_LIST
+            ),
+            Feature(
+                name="customer2_past_transactions_float", dtype=ValueType.FLOAT_LIST
+            ),
+            Feature(
+                name="customer2_past_transactions_string", dtype=ValueType.STRING_LIST
+            ),
+            Feature(name="customer2_past_transactions_bool", dtype=ValueType.BOOL_LIST),
+        ],
+        entities=[Entity("customer_id2", ValueType.INT64)],
+        max_age=Duration(seconds=3600),
+    )
+
+    client.set_project(PROJECT_NAME)
+    client.apply(customer_fs)
+
+    customer_fs = client.get_feature_set(name="customer2")
+    client.ingest(customer_fs, nonlist_entity_dataframe, timeout=600)
+    time.sleep(15)
+
+    online_request_entity = [{"customer_id2": 0}, {"customer_id2": 1}]
+    online_request_features = [
+        "customer2_rating",
+        "customer2_cost",
+        "customer2_past_transactions_int",
+        "customer2_past_transactions_double",
+        "customer2_past_transactions_float",
+        "customer2_past_transactions_string",
+        "customer2_past_transactions_bool",
+    ]
+    online_request_entity2 = [
+        {"customer_id2": Value(int64_val=0)},
+        {"customer_id2": Value(int64_val=1)},
+    ]
+
+    def try_get_features1():
+        response = client.get_online_features(
+            entity_rows=online_request_entity, feature_refs=online_request_features
+        )
+        return response, True
+
+    def try_get_features2():
+        response = client.get_online_features(
+            entity_rows=online_request_entity2, feature_refs=online_request_features
+        )
+        return response, True
+
+    online_features_actual1 = wait_retry_backoff(
+        retry_fn=try_get_features1,
+        timeout_secs=90,
+        timeout_msg="Timed out trying to get online feature values",
+    )
+
+    online_features_actual2 = wait_retry_backoff(
+        retry_fn=try_get_features2,
+        timeout_secs=90,
+        timeout_msg="Timed out trying to get online feature values",
+    )
+
+    online_features_expected = {
+        "customer_id2": [0, 1],
+        "customer2_rating": [0, 1],
+        "customer2_cost": [0.5, 1.5],
+        "customer2_past_transactions_int": [[0, 2], [1, 3]],
+        "customer2_past_transactions_double": [[0.5, 2.0], [1.5, 3.0]],
+        "customer2_past_transactions_float": [[0.5, 2.0], [1.5, 3.0]],
+        "customer2_past_transactions_string": [
+            ["first_0", "second_0"],
+            ["first_1", "second_1"],
+        ],
+        "customer2_past_transactions_bool": [[True, False], [True, False]],
+    }
+
+    assert online_features_actual1.to_dict() == online_features_expected
+    assert online_features_actual2.to_dict() == online_features_expected
+
+    # Case 2: Feature retrieval with multiple entities retrieval check with mixed types
+    with pytest.raises(TypeError) as excinfo:
+        online_request_entity2 = [{"customer_id": 0}, {"customer_id": "error_pls"}]
+        online_features_actual2 = client.get_online_features(
+            entity_rows=online_request_entity2, feature_refs=online_request_features
+        )
+
+    assert (
+        "Input entity customer_id has mixed types, ValueType.STRING and ValueType.INT64. That is not allowed."
+        in str(excinfo.value)
+    )
+
+
+@pytest.mark.timeout(600)
+@pytest.mark.run(order=15)
+def test_basic_retrieve_online_entity_listform(client, list_entity_dataframe):
+    # Case 1: Features retrieval with entity in list format check
+    district_fs = FeatureSet(
+        name="district",
+        features=[
+            Feature(name="district_rating", dtype=ValueType.INT64),
+            Feature(name="district_cost", dtype=ValueType.FLOAT),
+            Feature(name="district_past_transactions_int", dtype=ValueType.INT64_LIST),
+            Feature(
+                name="district_past_transactions_double", dtype=ValueType.DOUBLE_LIST
+            ),
+            Feature(
+                name="district_past_transactions_float", dtype=ValueType.FLOAT_LIST
+            ),
+            Feature(
+                name="district_past_transactions_string", dtype=ValueType.STRING_LIST
+            ),
+            Feature(name="district_past_transactions_bool", dtype=ValueType.BOOL_LIST),
+        ],
+        entities=[Entity("district_ids", dtype=ValueType.INT64_LIST)],
+        max_age=Duration(seconds=3600),
+    )
+
+    client.set_project(PROJECT_NAME)
+    client.apply(district_fs)
+
+    district_fs = client.get_feature_set(name="district")
+    client.ingest(district_fs, list_entity_dataframe, timeout=600)
+    time.sleep(15)
+
+    online_request_entity = [{"district_ids": [np.int64(1), np.int64(2), np.int64(3)]}]
+    online_request_features = [
+        "district_rating",
+        "district_cost",
+        "district_past_transactions_int",
+        "district_past_transactions_double",
+        "district_past_transactions_float",
+        "district_past_transactions_string",
+        "district_past_transactions_bool",
+    ]
+    online_request_entity2 = [
+        {"district_ids": Value(int64_list_val=Int64List(val=[1, 2, 3]))}
+    ]
+
+    def try_get_features1():
+        response = client.get_online_features(
+            entity_rows=online_request_entity, feature_refs=online_request_features
+        )
+        return response, True
+
+    def try_get_features2():
+        response = client.get_online_features(
+            entity_rows=online_request_entity2, feature_refs=online_request_features
+        )
+        return response, True
+
+    online_features_actual = wait_retry_backoff(
+        retry_fn=try_get_features1,
+        timeout_secs=90,
+        timeout_msg="Timed out trying to get online feature values",
+    )
+
+    online_features_actual2 = wait_retry_backoff(
+        retry_fn=try_get_features2,
+        timeout_secs=90,
+        timeout_msg="Timed out trying to get online feature values",
+    )
+
+    online_features_expected = {
+        "district_ids": [[np.int64(1), np.int64(2), np.int64(3)]],
+        "district_rating": [1],
+        "district_cost": [1.5],
+        "district_past_transactions_int": [[1, 3]],
+        "district_past_transactions_double": [[1.5, 3.0]],
+        "district_past_transactions_float": [[1.5, 3.0]],
+        "district_past_transactions_string": [["first_1", "second_1"]],
+        "district_past_transactions_bool": [[True, False]],
+    }
+
+    assert online_features_actual.to_dict() == online_features_expected
+    assert online_features_actual2.to_dict() == online_features_expected
+
+    # Case 2: Features retrieval with entity in list format check with mixed types
+    with pytest.raises(ValueError) as excinfo:
+        online_request_entity2 = [{"district_ids": [np.int64(1), np.int64(2), True]}]
+        online_features_actual2 = client.get_online_features(
+            entity_rows=online_request_entity2, feature_refs=online_request_features
+        )
+
+    assert (
+        "List value type for field district_ids is inconsistent. ValueType.INT64 different from ValueType.BOOL."
+        in str(excinfo.value)
+    )
+
+
 @pytest.mark.timeout(300)
 @pytest.mark.run(order=19)
 def test_basic_ingest_jobs(client):