diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb
index 91737c9..afcf2ca 100644
--- a/template/quickstart.ipynb
+++ b/template/quickstart.ipynb
@@ -71,7 +71,7 @@
     "    # Pull required modules from this example\n",
     "    !git clone -b main https://github.com/zenml-io/zenml\n",
     "    !cp -r zenml/examples/quickstart/* .\n",
-    "    !rm -rf zenml\n"
+    "    !rm -rf zenml"
    ]
   },
   {
@@ -84,6 +84,7 @@
     "!zenml integration install sklearn -y\n",
     "\n",
     "import IPython\n",
+    "\n",
     "IPython.Application.instance().kernel.do_shutdown(restart=True)"
    ]
   },
@@ -145,28 +146,22 @@
    "outputs": [],
    "source": [
     "# Do the imports at the top\n",
-    "from typing_extensions import Annotated\n",
-    "from sklearn.datasets import load_breast_cancer\n",
-    "\n",
     "import random\n",
-    "import pandas as pd\n",
-    "from zenml import step, pipeline, Model, get_step_context\n",
-    "from zenml.client import Client\n",
-    "from zenml.logger import get_logger\n",
+    "from typing import List, Optional\n",
     "from uuid import UUID\n",
     "\n",
-    "from typing import Optional, List\n",
-    "\n",
-    "from zenml import pipeline\n",
-    "\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_breast_cancer\n",
     "from steps import (\n",
     "    data_loader,\n",
     "    data_preprocessor,\n",
     "    data_splitter,\n",
+    "    inference_preprocessor,\n",
     "    model_evaluator,\n",
-    "    inference_preprocessor\n",
     ")\n",
-    "\n",
+    "from typing_extensions import Annotated\n",
+    "from zenml import Model, get_step_context, pipeline, step\n",
+    "from zenml.client import Client\n",
     "from zenml.logger import get_logger\n",
     "\n",
     "logger = get_logger(__name__)\n",
@@ -205,7 +200,7 @@
     "@step\n",
     "def data_loader_simplified(\n",
     "    random_state: int, is_inference: bool = False, target: str = \"target\"\n",
-    ") -> Annotated[pd.DataFrame, \"dataset\"]:  # We name the dataset \n",
+    ") -> Annotated[pd.DataFrame, \"dataset\"]:  # We name the dataset\n",
     "    \"\"\"Dataset reader step.\"\"\"\n",
     "    dataset = load_breast_cancer(as_frame=True)\n",
     "    inference_size = int(len(dataset.target) * 0.05)\n",
@@ -218,7 +213,7 @@
     "        dataset.drop(inference_subset.index, inplace=True)\n",
     "    dataset.reset_index(drop=True, inplace=True)\n",
     "    logger.info(f\"Dataset with {len(dataset)} records loaded!\")\n",
-    "    return dataset\n"
+    "    return dataset"
    ]
   },
   {
@@ -291,7 +286,7 @@
     "    normalize: Optional[bool] = None,\n",
     "    drop_columns: Optional[List[str]] = None,\n",
     "    target: Optional[str] = \"target\",\n",
-    "    random_state: int = 17\n",
+    "    random_state: int = 17,\n",
     "):\n",
     "    \"\"\"Feature engineering pipeline.\"\"\"\n",
     "    # Link all the steps together by calling them and passing the output\n",
@@ -402,7 +397,6 @@
     "from zenml.environment import Environment\n",
     "from zenml.zen_stores.rest_zen_store import RestZenStore\n",
     "\n",
-    "\n",
     "if not isinstance(client.zen_store, RestZenStore):\n",
     "    # Only spin up a local Dashboard in case you aren't already connected to a remote server\n",
     "    if Environment.in_google_colab():\n",
@@ -479,7 +473,9 @@
    "outputs": [],
    "source": [
     "# Get artifact version from our run\n",
-    "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"] \n",
+    "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\n",
+    "    \"dataset_trn\"\n",
+    "]\n",
     "\n",
     "# Get latest version from client directly\n",
     "dataset_trn_artifact_version = client.get_artifact_version(\"dataset_trn\")\n",
@@ -498,7 +494,9 @@
    "source": [
     "# Fetch the rest of the artifacts\n",
     "dataset_tst_artifact_version = client.get_artifact_version(\"dataset_tst\")\n",
-    "preprocessing_pipeline_artifact_version = client.get_artifact_version(\"preprocess_pipeline\")"
+    "preprocessing_pipeline_artifact_version = client.get_artifact_version(\n",
+    "    \"preprocess_pipeline\"\n",
+    ")"
    ]
   },
   {
@@ -576,7 +574,9 @@
     "def model_trainer(\n",
     "    dataset_trn: pd.DataFrame,\n",
     "    model_type: str = \"sgd\",\n",
-    ") -> Annotated[ClassifierMixin, ArtifactConfig(name=\"sklearn_classifier\", is_model_artifact=True)]:\n",
+    ") -> Annotated[\n",
+    "    ClassifierMixin, ArtifactConfig(name=\"sklearn_classifier\", is_model_artifact=True)\n",
+    "]:\n",
     "    \"\"\"Configure and train a model on the training dataset.\"\"\"\n",
     "    target = \"target\"\n",
     "    if model_type == \"sgd\":\n",
@@ -584,7 +584,7 @@
     "    elif model_type == \"rf\":\n",
     "        model = RandomForestClassifier()\n",
     "    else:\n",
-    "        raise ValueError(f\"Unknown model type {model_type}\")   \n",
+    "        raise ValueError(f\"Unknown model type {model_type}\")\n",
     "\n",
     "    logger.info(f\"Training model {model}...\")\n",
     "\n",
@@ -592,7 +592,7 @@
     "        dataset_trn.drop(columns=[target]),\n",
     "        dataset_trn[target],\n",
     "    )\n",
-    "    return model\n"
+    "    return model"
    ]
   },
   {
@@ -630,14 +630,14 @@
     "    min_train_accuracy: float = 0.0,\n",
     "    min_test_accuracy: float = 0.0,\n",
     "):\n",
-    "    \"\"\"Model training pipeline.\"\"\" \n",
+    "    \"\"\"Model training pipeline.\"\"\"\n",
     "    if train_dataset_id is None or test_dataset_id is None:\n",
-    "        # If we dont pass the IDs, this will run the feature engineering pipeline   \n",
+    "        # If we dont pass the IDs, this will run the feature engineering pipeline\n",
     "        dataset_trn, dataset_tst = feature_engineering()\n",
     "    else:\n",
     "        # Load the datasets from an older pipeline\n",
     "        dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n",
-    "        dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id) \n",
+    "        dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id)\n",
     "\n",
     "    trained_model = model_trainer(\n",
     "        dataset_trn=dataset_trn,\n",
@@ -676,7 +676,7 @@
     "training(\n",
     "    model_type=\"rf\",\n",
     "    train_dataset_id=dataset_trn_artifact_version.id,\n",
-    "    test_dataset_id=dataset_tst_artifact_version.id\n",
+    "    test_dataset_id=dataset_tst_artifact_version.id,\n",
     ")\n",
     "\n",
     "rf_run = client.get_pipeline(\"training\").last_run"
@@ -693,7 +693,7 @@
     "sgd_run = training(\n",
     "    model_type=\"sgd\",\n",
     "    train_dataset_id=dataset_trn_artifact_version.id,\n",
-    "    test_dataset_id=dataset_tst_artifact_version.id\n",
+    "    test_dataset_id=dataset_tst_artifact_version.id,\n",
     ")\n",
     "\n",
     "sgd_run = client.get_pipeline(\"training\").last_run"
@@ -717,7 +717,9 @@
    "outputs": [],
    "source": [
     "# The evaluator returns a float value with the accuracy\n",
-    "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()"
+    "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\n",
+    "    \"model_evaluator\"\n",
+    "].output.load()"
    ]
   },
   {
@@ -776,7 +778,7 @@
     "training_configured(\n",
     "    model_type=\"sgd\",\n",
     "    train_dataset_id=dataset_trn_artifact_version.id,\n",
-    "    test_dataset_id=dataset_tst_artifact_version.id\n",
+    "    test_dataset_id=dataset_tst_artifact_version.id,\n",
     ")"
    ]
   },
@@ -798,7 +800,7 @@
     "training_configured(\n",
     "    model_type=\"rf\",\n",
     "    train_dataset_id=dataset_trn_artifact_version.id,\n",
-    "    test_dataset_id=dataset_tst_artifact_version.id\n",
+    "    test_dataset_id=dataset_tst_artifact_version.id,\n",
     ")"
    ]
   },
@@ -848,7 +850,9 @@
     "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n",
     "\n",
     "# We can now load our classifier directly as well\n",
-    "random_forest_classifier = rf_zenml_model_version.get_artifact(\"sklearn_classifier\").load()\n",
+    "random_forest_classifier = rf_zenml_model_version.get_artifact(\n",
+    "    \"sklearn_classifier\"\n",
+    ").load()\n",
     "\n",
     "random_forest_classifier"
    ]
@@ -956,7 +960,7 @@
     "\n",
     "    predictions = pd.Series(predictions, name=\"predicted\")\n",
     "\n",
-    "    return predictions\n"
+    "    return predictions"
    ]
   },
   {
@@ -983,18 +987,18 @@
     "    random_state = 42\n",
     "    target = \"target\"\n",
     "\n",
-    "    df_inference = data_loader(\n",
-    "        random_state=random_state, is_inference=True\n",
-    "    )\n",
+    "    df_inference = data_loader(random_state=random_state, is_inference=True)\n",
     "    df_inference = inference_preprocessor(\n",
     "        dataset_inf=df_inference,\n",
     "        # We use the preprocess pipeline from the feature engineering pipeline\n",
-    "        preprocess_pipeline=client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id),\n",
+    "        preprocess_pipeline=client.get_artifact_version(\n",
+    "            name_id_or_prefix=preprocess_pipeline_id\n",
+    "        ),\n",
     "        target=target,\n",
     "    )\n",
     "    inference_predict(\n",
     "        dataset_inf=df_inference,\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -1018,7 +1022,7 @@
     "# Lets add some metadata to the model to make it identifiable\n",
     "pipeline_settings[\"model\"] = Model(\n",
     "    name=\"breast_cancer_classifier\",\n",
-    "    version=\"production\", # We can pass in the stage name here!\n",
+    "    version=\"production\",  # We can pass in the stage name here!\n",
     "    license=\"Apache 2.0\",\n",
     "    description=\"A breast cancer classifier\",\n",
     "    tags=[\"breast_cancer\", \"classifier\"],\n",
@@ -1039,9 +1043,7 @@
     "# Let's run it again to make sure we have two versions\n",
     "# We need to pass in the ID of the preprocessing done in the feature engineering pipeline\n",
     "# in order to avoid training-serving skew\n",
-    "inference_configured(\n",
-    "    preprocess_pipeline_id=preprocessing_pipeline_artifact_version.id\n",
-    ")"
+    "inference_configured(preprocess_pipeline_id=preprocessing_pipeline_artifact_version.id)"
    ]
   },
   {
@@ -1061,7 +1063,9 @@
    "outputs": [],
    "source": [
     "# Fetch production model\n",
-    "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n",
+    "production_model_version = client.get_model_version(\n",
+    "    \"breast_cancer_classifier\", \"production\"\n",
+    ")\n",
     "\n",
     "# Get the predictions artifact\n",
     "production_model_version.get_artifact(\"predictions\").load()"