diff --git a/.gitignore b/.gitignore
index 218d2bf..370b0c9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -129,4 +129,6 @@ dmypy.json
 .pyre/
 
 # Trained models
-*trained_models*
+mlflow_artifacts/
+mlflow_trained_models/
+trained_models/
diff --git a/examples/01. Train a model using high-level pipelines.ipynb b/examples/01. Train a model using high-level pipelines.ipynb
index 9968ac8..3d3da63 100644
--- a/examples/01. Train a model using high-level pipelines.ipynb	
+++ b/examples/01. Train a model using high-level pipelines.ipynb	
@@ -10,8 +10,9 @@
     }
    },
    "source": [
-    "# Example to train a model\n",
-    "Using the openstf tasks"
+    "# Train a model\n",
+    "In this example notebook, a model is trained for a location with id '287'. The data for this location can be found in the 'data' folder. \n",
+    "First, the prediction job will be defined, which contains the properties of the training and prediction. Thereafter, the model can be trained by the ```train_model_pipeline()```. "
    ]
   },
   {
@@ -30,7 +31,8 @@
     "import IPython\n",
     "from openstef.pipeline.train_model import train_model_pipeline\n",
     "from openstef.pipeline.create_forecast import create_forecast_pipeline\n",
-    "from openstef.data_classes.prediction_job import PredictionJobDataClass"
+    "from openstef.data_classes.prediction_job import PredictionJobDataClass\n",
+    "\n"
    ]
   },
   {
@@ -45,9 +47,9 @@
    },
    "outputs": [],
    "source": [
-    "# define properties of training/prediction. We call this a 'prediction_job'\n",
+    "# Define properties of training/prediction. We call this a 'prediction_job'\n",
     "pj = dict(id=287,\n",
-    "        model='xgb',\n",
+    "        model='xgb', \n",
     "        quantiles=[10,30,50,70,90],\n",
     "        forecast_type=\"demand\",\n",
     "        lat=52.0,\n",
@@ -81,6 +83,8 @@
    },
    "outputs": [],
    "source": [
+    "# Print the train data. \n",
+    "# For every timestamp, bot the load as well as feature data is available. \n",
     "train_data.head()"
    ]
   },
@@ -105,7 +109,7 @@
    "metadata": {},
    "source": [
     "# Train a model\n",
-    "Train a model using the high-level pipeline. Store the model and reports on training proces in ./trained_models"
+    "Train the model by using the high-level pipelin ```train_model_pipeline```. Store the model and reports on training proces in ./trained_models by setting mlflow_tracking_uri and artifact_folder to this path. "
    ]
   },
   {
@@ -125,8 +129,8 @@
     "    pj,\n",
     "    train_data,\n",
     "    check_old_model_age=False,\n",
-    "    mlflow_tracking_uri=\"./trained_models\",\n",
-    "    artifact_folder=\"./trained_models\",\n",
+    "    mlflow_tracking_uri=\"./mlflow_trained_models\",\n",
+    "    artifact_folder=\"./mlflow_artifacts\",\n",
     "    )"
    ]
   },
@@ -135,7 +139,8 @@
    "id": "7209dca5",
    "metadata": {},
    "source": [
-    "You can find the trained model in ./trained_models, along with reports on the training process"
+    "You can find the trained model in ./trained_models, along with reports on the training process. Below the Predictor0.25 and Predictor47.0 plots are shown, as well as the weight plot. The predictor plots show {nog invullen}.\n",
+    "The weight plot shows the importance and weight of every feature."
    ]
   },
   {
@@ -155,6 +160,28 @@
     "                     f\"<iframe src=./trained_models/{pj['id']}/Predictor47.0.html width=800 height=400></iframe>\"\n",
     "                     f\"<iframe src=./trained_models/{pj['id']}/weight_plot.html width=800 height=400></iframe>\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "990f046f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "html_file_path=\"examples\\\\mlflow_artifacts\\*\"\n",
+    "with open(html_file_path, 'r') as f:\n",
+    "    html_content = f.read()\n",
+    "\n",
+    "import plotly.io as pio\n",
+    "\n",
+    "# This ensures Plotly output works in multiple places:\n",
+    "# plotly_mimetype: VS Code notebook UI\n",
+    "# notebook: \"Jupyter: Export to HTML\" command in VS Code\n",
+    "# See https://plotly.com/python/renderers/#multiple-renderers\n",
+    "pio.renderers.default = \"plotly_mimetype+notebook\"\n",
+    "\n",
+    "fig= go.Figure(data=html_content)"
+   ]
   }
  ],
  "metadata": {
@@ -173,7 +200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.13"
   },
   "toc": {
    "base_numbering": 1,