diff --git a/tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb b/tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb
index f82604832a..f83dc6452e 100644
--- a/tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb
+++ b/tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb
@@ -344,8 +344,8 @@
                 "    description=\"Custom environment for Credit Card Defaults pipeline\",\n",
                 "    tags={\"scikit-learn\": \"0.24.2\"},\n",
                 "    conda_file=os.path.join(dependencies_dir, \"conda.yaml\"),\n",
-                "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
-                "    version=\"0.1.1\",\n",
+                "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n",
+                "    version=\"0.1.2\",\n",
                 ")\n",
                 "pipeline_job_env = ml_client.environments.create_or_update(pipeline_job_env)\n",
                 "\n",
@@ -610,6 +610,7 @@
                 "import os\n",
                 "import pandas as pd\n",
                 "import mlflow\n",
+                "from mlflow.exceptions import MlflowException\n",
                 "\n",
                 "\n",
                 "def select_first_file(path):\n",
@@ -627,7 +628,7 @@
                 "mlflow.start_run()\n",
                 "\n",
                 "# enable autologging\n",
-                "mlflow.sklearn.autolog()\n",
+                "mlflow.sklearn.autolog(log_models=False)\n",
                 "\n",
                 "os.makedirs(\"./outputs\", exist_ok=True)\n",
                 "\n",
@@ -674,26 +675,31 @@
                 "\n",
                 "    print(classification_report(y_test, y_pred))\n",
                 "\n",
-                "    # Registering the model to the workspace\n",
-                "    print(\"Registering the model via MLFlow\")\n",
-                "    mlflow.sklearn.log_model(\n",
-                "        sk_model=clf,\n",
-                "        registered_model_name=args.registered_model_name,\n",
-                "        artifact_path=args.registered_model_name,\n",
-                "    )\n",
-                "\n",
                 "    # Saving the model to a file\n",
+                "    model_path = os.path.join(args.model, \"trained_model\")\n",
                 "    mlflow.sklearn.save_model(\n",
                 "        sk_model=clf,\n",
-                "        path=os.path.join(args.model, \"trained_model\"),\n",
+                "        path=model_path,\n",
                 "    )\n",
                 "\n",
+                "    # Registering the model to the workspace\n",
+                "    print(\"Registering the model via MLFlow\")\n",
+                "    mlflow.log_artifacts(model_path, artifact_path=args.registered_model_name)\n",
+                "    run_id = mlflow.active_run().info.run_id\n",
+                "    model_uri = f\"runs:/{run_id}/{args.registered_model_name}\"\n",
+                "    client = mlflow.tracking.MlflowClient()\n",
+                "    try:\n",
+                "        client.create_registered_model(args.registered_model_name)\n",
+                "    except mlflow.exceptions.MlflowException:\n",
+                "        pass  # model already exists\n",
+                "    client.create_model_version(args.registered_model_name, model_uri, run_id)\n",
+                "\n",
                 "    # Stop Logging\n",
                 "    mlflow.end_run()\n",
                 "\n",
                 "\n",
                 "if __name__ == \"__main__\":\n",
-                "    main()"
+                "    main()\n"
             ]
         },
         {