diff --git a/e2e-tutorials/image_classification/notebooks/30-CreatePipelinePackage.ipynb b/e2e-tutorials/image_classification/notebooks/30-CreatePipelinePackage.ipynb
index f88e39a..68215a8 100644
--- a/e2e-tutorials/image_classification/notebooks/30-CreatePipelinePackage.ipynb
+++ b/e2e-tutorials/image_classification/notebooks/30-CreatePipelinePackage.ipynb
@@ -1,258 +1,258 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-        "SPDX-License-Identifier": "MIT"
-      },
-      "source": [
-        "# Create a TensorFlow Lite edge configuration package\n",
-        "\n",
-        "In this notebook, the main goal is to create a pipeline with all of the contents that are necessary for the execution of the model on an Industrial Edge device.  \n",
-        "In order to put the elements together, this example collects files\n",
-        "\n",
-        "from [10-CreateClassificationModel](10-CreateClassificationModel.ipynb) notebook:  \n",
-        "- **classification_mobilnet.tflite**: the trained model for classification with TFlite\n",
-        "\n",
-        "from [20-CreateInferenceWrapper](20-CreateInferenceWrapper.ipynb) notebook:  \n",
-        "- **entrypoint.py**: the script that is called by the runtime to execute the model on the Edge side\n",
-        "- **payload.py**: contains the method which extracts the payload and create a PIL Image to be processed\n",
-        "- **vision_classifier.py**: contains the method to utilize the model and produces a prediction"
-      ]
+    "cells": [
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {
+                "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
+                "SPDX-License-Identifier": "MIT"
+            },
+            "source": [
+                "# Create a TensorFlow Lite edge configuration package\n",
+                "\n",
+                "In this notebook, the main goal is to create a pipeline with all of the contents that are necessary for the execution of the model on an Industrial Edge device.  \n",
+                "In order to put the elements together, this example collects files\n",
+                "\n",
+                "from [10-CreateClassificationModel](10-CreateClassificationModel.ipynb) notebook:  \n",
+                "- **classification_mobilnet.tflite**: the trained model for classification with TFlite\n",
+                "\n",
+                "from [20-CreateInferenceWrapper](20-CreateInferenceWrapper.ipynb) notebook:  \n",
+                "- **entrypoint.py**: the script that is called by the runtime to execute the model on the Edge side\n",
+                "- **payload.py**: contains the method which extracts the payload and create a PIL Image to be processed\n",
+                "- **vision_classifier.py**: contains the method to utilize the model and produces a prediction"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Please note, there is no official TFLite runtime for Windows."
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Imports  "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from simaticai import deployment"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Create a single component\n",
+                "\n",
+                "In this step, we create a `PythonComponent` that reads the input data `vision_payload` by `entrypoint.py`, processes the images by `vision_classifier.py` with model `classification_mobilnet.tflite` and produces an output `prediction` by `entrypoint.py` script."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "COMPONENT_DESCRIPTION =\"\"\"\\\n",
+                "This component uses a trained TensorFlow Lite image classification model that reads the vision_payload input and produces a prediction as an output.\n",
+                "\"\"\"\n",
+                "INPUT_DESCRIPTION =\"\"\"\n",
+                "Vision connector MQTT payload holding the image to be classified.\n",
+                "\"\"\"\n",
+                "OUTPUT_DESCRIPTION =\"\"\"\n",
+                "The most probable class predicted for the image as an integer string.\n",
+                "\"\"\"\n",
+                "\n",
+                "component = deployment.PythonComponent(\n",
+                "    name='inference', \n",
+                "    python_version='3.11',\n",
+                "    desc=COMPONENT_DESCRIPTION)\n",
+                "\n",
+                "component.add_resources('..', 'entrypoint.py')\n",
+                "component.set_entrypoint('entrypoint.py')\n",
+                "component.add_resources('..', ['src/payload.py', 'src/vision_classifier.py'])\n",
+                "\n",
+                "component.add_input('vision_payload', 'ImageSet')\n",
+                "component.add_output('prediction', 'String')"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "#### Add metrics\n",
+                "It can be useful to monitor the pipeline, e.g. watch the prediction probabilities. The metric name must contain an underscore (`_`), because the part before the underscore is used to group custom metrics on the dashboard.\n",
+                "\n",
+                "**⚠ Remember!**\n",
+                "You have to use the same names here and in the inference wrapper script."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "component.add_metric(\"ic_probability\")"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Add dependencies\n",
+                "\n",
+                "All of the required dependencies are collected in the file `runtime_requirements.txt`. See [How to handle Python dependencies](../../../howto-guides/04-handle-python-dependencies.md) for more possibilities."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "component.set_requirements('../runtime_requirements-py3.11.txt')"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Add a model"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "component.add_resources('..', 'models/classification_mobilnet.tflite')"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Create a pipeline from this component\n",
+                "\n",
+                "Now you can use the component to create a pipeline configuration.  "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "tags": [
+                    "testinit_package_name"
+                ]
+            },
+            "outputs": [],
+            "source": [
+                "PIPELINE_DESCRIPTION =\"\"\"\\\n",
+                "This pipeline runs a TensorFlow Lite model on an Industrial Edge device.\n",
+                "The model was trained to recognize and classify images of following Siemens SIMATIC automation products: ET 200AL, ET 200eco PN, ET 200SP, S7-1200, S7-1500.\n",
+                "\n",
+                "The pipeline is designed to be fed from Vision Connector via Databus with PNG or JPEG type images.\n",
+                "The pipeline output is to be sent to the Databus.\n",
+                "\"\"\"\n",
+                "\n",
+                "#To assure compatibility with older versions of AI SDK (<v1.5.0), you must define the `version` parameter in the `from_components()` method\n",
+                "pipeline = deployment.Pipeline.from_components([component], name='ImageClassification', desc=PIPELINE_DESCRIPTION)\n",
+                "pipeline"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Build the edge configuration package\n",
+                "\n",
+                "This step creates the proper content in the defined target folder and creates the edge configuration package as a zip file.  \n",
+                "The export method first validates the pipeline and raises an error if it finds any problems. Manual validation is also possible with the `pipeline.validate()` method.\n",
+                "\n",
+                "Edge packages are identified by their `package id` and `version` attributes, and will be grouped in AI Inference Server and in other Edge applications by `package id`.\n",
+                "When saving a pipeline - in the `export()` method - you can specify a `pacakge id` in a UUID 4 compliant format, or an automatically generated one will be assigned. \n",
+                "If no `package id` is defined in the `export()` method, and AI SDK finds an already assigned `package id` in a previously generated, and similarly named package, the `package id` found in the latest package will be used.\n",
+                "\n",
+                "AI SDK will automatically assign and increase the version number of a pipeline every time a package is saved, unless a new package id is assigned in the `export()` method or an explicit version number is defined without package id either in the `export()` method or in the pipeline constructor.\n",
+                "\n",
+                "Restrictions: \n",
+                "- You can not overwrite a previously saved package with the same `package id` if the `package id` is explicitly assigned in the `export()` method \n",
+                "- Packages generated with older versions of AI SDK (without `package id`) will be overwritten\n",
+                "- If a new `package id` is assigned to an existing version of the package it will overwrite the old one\n",
+                "- If no previous version of a package (with a generated or explicitly assigned `package id`) found, AI SDK will assign the version `1` to the created package\n",
+                "- Version defined in the `export()` method takes precedence over the version assigned on constructor level\n",
+                "\n",
+                "Now you are ready to bring your model to the shopfloor by building the edge configuration package from the pipeline configuration. You can achieve this by the following step."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "edge_package_path = pipeline.export(destination = '../packages')\n",
+                "edge_package_path"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Test the edge configuration package locally\n",
+                "\n",
+                "We suggest you test your edge configuration package on your computer before deploying it to an Edge device. It is possible to do so using notebook [40-TestPipelineLocally.ipynb](40-TestPipelineLocally.ipynb)."
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Test the edge configuration package in AI Inference Server\n",
+                "\n",
+                "If you also have dockerized Edge environment on your computer, why not try your package with how-to guide [22-test-on-edge-device](../../../howto-guides/22-test-on-edge-device.md)?  \n",
+                "This test can most accurately prove the validity of your package."
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python (image_classification)",
+            "language": "python",
+            "name": "image_classification"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.9"
+        }
     },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please note, there is no official TFLite runtime for Windows."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Imports  "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from simaticai import deployment"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a single component\n",
-        "\n",
-        "In this step, we create a `PythonComponent` that reads the input data `vision_payload` by `entrypoint.py`, processes the images by `vision_classifier.py` with model `classification_mobilnet.tflite` and produces an output `prediction` by `entrypoint.py` script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "COMPONENT_DESCRIPTION =\"\"\"\\\n",
-        "This component uses a trained TensorFlow Lite image classification model that reads the vision_payload input and produces a prediction as an output.\n",
-        "\"\"\"\n",
-        "INPUT_DESCRIPTION =\"\"\"\n",
-        "Vision connector MQTT payload holding the image to be classified.\n",
-        "\"\"\"\n",
-        "OUTPUT_DESCRIPTION =\"\"\"\n",
-        "The most probable class predicted for the image as an integer string.\n",
-        "\"\"\"\n",
-        "\n",
-        "component = deployment.PythonComponent(\n",
-        "    name='inference', \n",
-        "    python_version='3.11',\n",
-        "    desc=COMPONENT_DESCRIPTION)\n",
-        "\n",
-        "component.add_resources('..', 'entrypoint.py')\n",
-        "component.set_entrypoint('entrypoint.py')\n",
-        "component.add_resources('..', ['src/payload.py', 'src/vision_classifier.py'])\n",
-        "\n",
-        "component.add_input('vision_payload', 'ImageSet')\n",
-        "component.add_output('prediction', 'String')"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Add metrics\n",
-        "It can be useful to monitor the pipeline, e.g. watch the prediction probabilities. The metric name must contain an underscore (`_`), because the part before the underscore is used to group custom metrics on the dashboard.\n",
-        "\n",
-        "**&#9888; Remember!**\n",
-        "You have to use the same names here and in the inference wrapper script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "component.add_metric(\"ic_probability\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Add dependencies\n",
-        "\n",
-        "All of the required dependencies are collected in the file `runtime_requirements.txt`. See [How to handle Python dependencies](../../../howto-guides/04-handle-python-dependencies.md) for more possibilities."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "component.set_requirements('../runtime_requirements-py3.11.txt')"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Add a model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "component.add_resources('..', 'models/classification_mobilnet.tflite')"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a pipeline from this component\n",
-        "\n",
-        "Now you can use the component to create a pipeline configuration.  "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "testinit_package_name"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "PIPELINE_DESCRIPTION =\"\"\"\\\n",
-        "This pipeline runs a TensorFlow Lite model on an Industrial Edge device.\n",
-        "The model was trained to recognize and classify images of following Siemens SIMATIC automation products: ET 200AL, ET 200eco PN, ET 200SP, S7-1200, S7-1500.\n",
-        "\n",
-        "The pipeline is designed to be fed from Vision Connector via Databus with PNG or JPEG type images.\n",
-        "The pipeline output is to be sent to the Databus.\n",
-        "\"\"\"\n",
-        "\n",
-        "#To assure compatibility with older versions of AI SDK (<v1.5.0), you must define the `version` parameter in the `from_components()` method\n",
-        "pipeline = deployment.Pipeline.from_components([component], name='ImageClassification', desc=PIPELINE_DESCRIPTION)\n",
-        "pipeline"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Build the edge configuration package\n",
-        "\n",
-        "This step creates the proper content in the defined target folder and creates the edge configuration package as a zip file.  \n",
-        "The export method first validates the pipeline and raises an error if it finds any problems. Manual validation is also possible with the `pipeline.validate()` method.\n",
-        "\n",
-        "Edge packages are identified by their `package id` and `version` attributes, and will be grouped in AI Inference Server and in other Edge applications by `package id`.\n",
-        "When saving a pipeline - in the `export()` method - you can specify a `pacakge id` in a UUID 4 compliant format, or an automatically generated one will be assigned. \n",
-        "If no `package id` is defined in the `export()` method, and AI SDK finds an already assigned `package id` in a previously generated, and similarly named package, the `package id` found in the latest package will be used.\n",
-        "\n",
-        "AI SDK will automatically assign and increase the version number of a pipeline every time a package is saved, unless a new package id is assigned in the `export()` method or an explicit version number is defined without package id either in the `export()` method or in the pipeline constructor.\n",
-        "\n",
-        "Restrictions: \n",
-        "- You can not overwrite a previously saved package with the same `package id` if the `package id` is explicitly assigned in the `export()` method \n",
-        "- Packages generated with older versions of AI SDK (without `package id`) will be overwritten\n",
-        "- If a new `package id` is assigned to an existing version of the package it will overwrite the old one\n",
-        "- If no previous version of a package (with a generated or explicitly assigned `package id`) found, AI SDK will assign the version `1` to the created package\n",
-        "- Version defined in the `export()` method takes precedence over the version assigned on constructor level\n",
-        "\n",
-        "Now you are ready to bring your model to the shopfloor by building the edge configuration package from the pipeline configuration. You can achieve this by the following step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "edge_package_path = pipeline.export(destination = '../packages')\n",
-        "edge_package_path"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the edge configuration package locally\n",
-        "\n",
-        "We suggest you test your edge configuration package on your computer before deploying it to an Edge device. It is possible to do so using notebook [40-TestPipelineLocally.ipynb](40-TestPipelineLocally.ipynb)."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the edge configuration package in AI Inference Server\n",
-        "\n",
-        "If you also have dockerized Edge environment on your computer, why not try your package with how-to guide [22-test-on-edge-device](../../../howto-guides/22-test-on-edge-device.md)?  \n",
-        "This test can most accurately prove the validity of your package."
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python (image_classification)",
-      "language": "python",
-      "name": "image_classification"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.9"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
+    "nbformat": 4,
+    "nbformat_minor": 4
 }