diff --git a/data/mltable/test/MLTable b/data/mltable/test/MLTable
new file mode 100644
index 0000000..5996177
--- /dev/null
+++ b/data/mltable/test/MLTable
@@ -0,0 +1,6 @@
+$schema: http://azureml/sdk-2-0/MLTable.json
+type: mltable
+paths: 
+  - file: ./nyc_taxi_dataset_test.parquet
+transformations: 
+  - read_parquet
\ No newline at end of file
diff --git a/data/mltable/test/nyc_taxi_dataset_test.parquet b/data/mltable/test/nyc_taxi_dataset_test.parquet
new file mode 100644
index 0000000..c8271f7
Binary files /dev/null and b/data/mltable/test/nyc_taxi_dataset_test.parquet differ
diff --git a/data/mltable/train/MLTable b/data/mltable/train/MLTable
new file mode 100644
index 0000000..4f1e278
--- /dev/null
+++ b/data/mltable/train/MLTable
@@ -0,0 +1,6 @@
+$schema: http://azureml/sdk-2-0/MLTable.json
+type: mltable
+paths: 
+  - file: ./nyc_taxi_dataset_train.parquet
+transformations: 
+  - read_parquet
\ No newline at end of file
diff --git a/data/mltable/train/nyc_taxi_dataset_train.parquet b/data/mltable/train/nyc_taxi_dataset_train.parquet
new file mode 100644
index 0000000..409a574
Binary files /dev/null and b/data/mltable/train/nyc_taxi_dataset_train.parquet differ
diff --git a/notebooks/create-responsible-ai-dashboard.ipynb b/notebooks/create-responsible-ai-dashboard.ipynb
new file mode 100644
index 0000000..694bfa7
--- /dev/null
+++ b/notebooks/create-responsible-ai-dashboard.ipynb
@@ -0,0 +1,510 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "gather": {
+          "logged": 1672477533184
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# 実行する AML workspace の情報を設定\n",
+        "subscription_id = \"\"\n",
+        "resource_group = \"\"\n",
+        "workspace = \"\"\n",
+        "\n",
+        "rai_diabetes_regression_example_version_string = \"1\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "gather": {
+          "logged": 1672477533396
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from azure.ai.ml import dsl, Input\n",
+        "from azure.ai.ml.constants import AssetTypes\n",
+        "from azure.ai.ml import MLClient\n",
+        "from azure.identity import DefaultAzureCredential"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "gather": {
+          "logged": 1672477533664
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MLClient(credential=<azure.identity._credentials.default.DefaultAzureCredential object at 0x7f17980eb4f0>,\n",
+            "         subscription_id=27a05524-7a59-453b-88d7-df3ebaa2bcc1,\n",
+            "         resource_group_name=azuremlytate081922,\n",
+            "         workspace_name=azuremlwsytate081922)\n"
+          ]
+        }
+      ],
+      "source": [
+        "credential = DefaultAzureCredential()\n",
+        "ml_client = MLClient(\n",
+        "    credential=credential,\n",
+        "    subscription_id=subscription_id,\n",
+        "    resource_group_name=resource_group,\n",
+        "    workspace_name=workspace,\n",
+        ")\n",
+        "print(ml_client)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "gather": {
+          "logged": 1672477534177
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MLClient(credential=<azure.identity._credentials.default.DefaultAzureCredential object at 0x7f17980eb4f0>,\n",
+            "         subscription_id=6c6683e9-e5fe-4038-8519-ce6ebec2ba15,\n",
+            "         resource_group_name=registry-builtin-prod-eastus-01,\n",
+            "         workspace_name=None)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# RAI 組み込みコンポーネントの azureml レジストリへのハンドルを取得\n",
+        "registry_name = \"azureml\"\n",
+        "ml_client_registry = MLClient(\n",
+        "    credential=credential,\n",
+        "    subscription_id=subscription_id,\n",
+        "    resource_group_name=resource_group,\n",
+        "    registry_name=registry_name,\n",
+        ")\n",
+        "print(ml_client_registry)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "gather": {
+          "logged": 1672477534382
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azure.ai.ml.entities import Data\n",
+        "from azure.ai.ml.constants import AssetTypes\n",
+        "\n",
+        "train_data_path = \"../data/mltable/train/\"\n",
+        "test_data_path = \"../data/mltable/test/\"\n",
+        "input_train_data = \"nyc_taxi_train_mltable_dataset\"\n",
+        "input_test_data = \"nyc_taxi_test_mltable_dataset\"\n",
+        "\n",
+        "# train, test のデータアセットがなければ作成\n",
+        "try:\n",
+        "    train_data = ml_client.data.get(\n",
+        "        name=input_train_data, version=rai_diabetes_regression_example_version_string\n",
+        "    )\n",
+        "    test_data = ml_client.data.get(\n",
+        "        name=input_test_data, version=rai_diabetes_regression_example_version_string\n",
+        "    )\n",
+        "except Exception as e:\n",
+        "    train_data = Data(\n",
+        "        path=train_data_path,\n",
+        "        type=AssetTypes.MLTABLE,\n",
+        "        description=\"RAI diabetes regression example training data\",\n",
+        "        name=input_train_data,\n",
+        "        version=rai_diabetes_regression_example_version_string,\n",
+        "    )\n",
+        "    ml_client.data.create_or_update(train_data)\n",
+        "\n",
+        "    test_data = Data(\n",
+        "        path=test_data_path,\n",
+        "        type=AssetTypes.MLTABLE,\n",
+        "        description=\"RAI diabetes regression example test data\",\n",
+        "        name=input_test_data,\n",
+        "        version=rai_diabetes_regression_example_version_string,\n",
+        "    )\n",
+        "    ml_client.data.create_or_update(test_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1672469603345
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "gather": {
+          "logged": 1672477534500
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "model_name = \"nyc_taxi_mlflow_rai\"\n",
+        "compute_name = \"cpu-cluster\"\n",
+        "target_column_name = \"totalAmount\"\n",
+        "model_name_suffix = 1\n",
+        "expected_model_id = f\"{model_name}:{model_name_suffix}\"\n",
+        "azureml_model_id = f\"azureml:{expected_model_id}\"\n",
+        "programmers_train_mltable = Input(\n",
+        "    type=\"mltable\",\n",
+        "    path=f\"azureml:nyc_taxi_train_mltable_dataset:1\",\n",
+        "    mode=\"download\",\n",
+        ")\n",
+        "programmers_test_mltable = Input(\n",
+        "    type=\"mltable\",\n",
+        "    path=f\"azureml:nyc_taxi_test_mltable_dataset:1\",\n",
+        "    mode=\"download\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "gather": {
+          "logged": 1672477537043
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The current version of RAI built-in components is: 0.4.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "label = \"latest\"\n",
+        "\n",
+        "# 最新バージョンのコンポーネントを取得\n",
+        "rai_constructor_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_insight_constructor\", label=label\n",
+        ")\n",
+        "\n",
+        "version = rai_constructor_component.version\n",
+        "print(\"The current version of RAI built-in components is: \" + version)\n",
+        "\n",
+        "rai_explanation_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_explanation\", version=version\n",
+        ")\n",
+        "\n",
+        "rai_causal_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_causal\", version=version\n",
+        ")\n",
+        "\n",
+        "rai_counterfactual_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_counterfactual\", version=version\n",
+        ")\n",
+        "\n",
+        "rai_erroranalysis_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_erroranalysis\", version=version\n",
+        ")\n",
+        "\n",
+        "rai_gather_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_insight_gather\", version=version\n",
+        ")\n",
+        "\n",
+        "rai_scorecard_component = ml_client_registry.components.get(\n",
+        "    name=\"microsoft_azureml_rai_tabular_score_card\", version=version\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "gather": {
+          "logged": 1672477537218
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# RAI パイプラインを構築\n",
+        "\n",
+        "categorical_columns = json.dumps(\n",
+        "    [\"vendorID\", \"passengerCount\"]\n",
+        ")\n",
+        "treatment_features = json.dumps([\"lpepPickupDatetime\", \"tripDistance\", \"pickupLongitude\", \"pickupLatitude\", \"dropoffLongitude\", \"dropoffLatitude\"])\n",
+        "desired_range = json.dumps([0, 290])\n",
+        "filter_columns = json.dumps([])\n",
+        "\n",
+        "\n",
+        "@dsl.pipeline(\n",
+        "    compute=compute_name,\n",
+        "    experiment_name=f\"rai_pipeline_nyc_taxi\",\n",
+        ")\n",
+        "def rai_pipeline_nyc_taxi(\n",
+        "    target_column_name,\n",
+        "    train_data,\n",
+        "    test_data\n",
+        "):\n",
+        "    # RAIInsights を開始\n",
+        "    create_rai_job = rai_constructor_component(\n",
+        "        title=\"RAI Dashboard Example\",\n",
+        "        task_type=\"regression\",\n",
+        "        model_info=expected_model_id,\n",
+        "        model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n",
+        "        train_dataset=train_data,\n",
+        "        test_dataset=test_data,\n",
+        "        target_column_name=target_column_name,\n",
+        "        categorical_column_names=categorical_columns,\n",
+        "    )\n",
+        "    create_rai_job.set_limits(timeout=120)\n",
+        "\n",
+        "    # モデルの説明を追加\n",
+        "    explain_job = rai_explanation_component(\n",
+        "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
+        "        comment=\"My comment\",\n",
+        "    )\n",
+        "    explain_job.set_limits(timeout=120)\n",
+        "\n",
+        "    # 因果分析を追加\n",
+        "    causal_job = rai_causal_component(\n",
+        "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
+        "        treatment_features=treatment_features,\n",
+        "    )\n",
+        "    causal_job.set_limits(timeout=180)\n",
+        "\n",
+        "    # 反事実分析を追加\n",
+        "    counterfactual_job = rai_counterfactual_component(\n",
+        "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
+        "        total_cfs=10,\n",
+        "        desired_range=desired_range,\n",
+        "    )\n",
+        "    counterfactual_job.set_limits(timeout=600)\n",
+        "\n",
+        "    # エラー分析を追加\n",
+        "    erroranalysis_job = rai_erroranalysis_component(\n",
+        "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
+        "        filter_features=filter_columns,\n",
+        "    )\n",
+        "    erroranalysis_job.set_limits(timeout=120)\n",
+        "\n",
+        "    # すべてを組み合わせ\n",
+        "    rai_gather_job = rai_gather_component(\n",
+        "        constructor=create_rai_job.outputs.rai_insights_dashboard,\n",
+        "        insight_1=explain_job.outputs.explanation,\n",
+        "        insight_2=causal_job.outputs.causal,\n",
+        "        insight_3=counterfactual_job.outputs.counterfactual,\n",
+        "        insight_4=erroranalysis_job.outputs.error_analysis,\n",
+        "    )\n",
+        "    rai_gather_job.set_limits(timeout=120)\n",
+        "\n",
+        "    rai_gather_job.outputs.dashboard.mode = \"upload\"\n",
+        "    rai_gather_job.outputs.ux_json.mode = \"upload\"\n",
+        "\n",
+        "    return {\n",
+        "        \"dashboard\": rai_gather_job.outputs.dashboard,\n",
+        "        \"ux_json\": rai_gather_job.outputs.ux_json\n",
+        "    }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "gather": {
+          "logged": 1672477537347
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "insights_pipeline_job = rai_pipeline_nyc_taxi(\n",
+        "    target_column_name=target_column_name,\n",
+        "    train_data=programmers_train_mltable,\n",
+        "    test_data=programmers_test_mltable\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "gather": {
+          "logged": 1672477540535
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "created_job = ml_client.jobs.create_or_update(insights_pipeline_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernel_info": {
+      "name": "python310-sdkv2"
+    },
+    "kernelspec": {
+      "display_name": "Python 3.10 - SDK V2",
+      "language": "python",
+      "name": "python310-sdkv2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.6"
+    },
+    "microsoft": {
+      "host": {
+        "AzureML": {
+          "notebookHasBeenCompleted": true
+        }
+      }
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
diff --git a/src/model/train.py b/src/model/train.py
index 74cd85c..6d5f58f 100644
--- a/src/model/train.py
+++ b/src/model/train.py
@@ -1,4 +1,4 @@
-import argparse
+import argparse
 import os
 
 import matplotlib.pyplot as plt
@@ -84,6 +84,11 @@ def save_model(model, output_dir):
     os.makedirs(os.path.join(output_dir, "models"), exist_ok=True)
     mlflow.sklearn.save_model(model, os.path.join(output_dir, "models"))
 
+    # RAI ダッシュボード用のモデル登録
+    mlflow.sklearn.log_model(sk_model=model, 
+                            registered_model_name="nyc_taxi_mlflow_rai", 
+                            artifact_path="models")
+
 
 def main(args):
     # 自動ロギングの有効化