From 33a4eacf1d31dc461afd0745f103800bb47b790c Mon Sep 17 00:00:00 2001
From: amlrelsa-ms <amlrelsa@microsoft.com>
Date: Wed, 30 Mar 2022 02:26:53 +0000
Subject: [PATCH] update samples from Release-131 as a part of  SDK release

---
 .../auto-ml-regression-model-proxy.ipynb      | 24 +++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
index 9ea4fd9b..518dc16d 100644
--- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
@@ -75,6 +75,7 @@
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -180,6 +181,29 @@
         "label = \"ERP\"\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The split data will be used in the remote compute by ModelProxy and locally to compare results.\n",
+        "So, we need to persist the split data to avoid descrepencies from different package versions in the local and remote."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "\n",
+        "train_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    train_data.to_pandas_dataframe(), target=(ds, \"machineTrainData\"), name=\"train_data\")\n",
+        "\n",
+        "test_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    test_data.to_pandas_dataframe(), target=(ds, \"machineTestData\"), name=\"test_data\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},