diff --git a/README.md b/README.md
index 01793905..be42aee0 100644
--- a/README.md
+++ b/README.md
@@ -48,6 +48,7 @@ In order to understand the tutorials you need to be familiar with general concep
     - [Credit Scores](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/credit_scores): Predict clients' repayment abilities.
     - [Electricity](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/electricity): Predict the electricity prices in several Swedish cities based on weather conditions, previous prices, and Swedish holidays.
     - [NYC Taxi Fares](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/nyc_taxi_fares): Predict the fare amount for a taxi ride in New York City given the pickup and dropoff locations.
+    - [Hospital Wait Time](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/hospital_wait_time): Predict the waiting time for a deceased donor kidney using Prophet model.
     - [Recommender System](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/recommender-system): Build a recommender system for fashion items.
     - [TimeSeries](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/timeseries): Timeseries price prediction.
     - [LLM PDF](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/advanced_tutorials/llm_pdfs): An AI assistant that utilizes a Retrieval-Augmented Generation (RAG) system to provide accurate answers to user questions by retrieving relevant context from PDF documents.
diff --git a/advanced_tutorials/hospital_wait_time/1_feature_pipeline.ipynb b/advanced_tutorials/hospital_wait_time/1_feature_pipeline.ipynb
new file mode 100644
index 00000000..ebf89dff
--- /dev/null
+++ b/advanced_tutorials/hospital_wait_time/1_feature_pipeline.ipynb
@@ -0,0 +1,597 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5615c2ae",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#ff5f27'> 📝 Imports </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fd527fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Mute warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f5d51d",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 💽 Data Loading</span>\n",
+    "\n",
+    "In this case, you are predicting the waiting time for a deceased donor kidney transplant involves estimating the duration a patient might need to wait from the time they are registered on the transplant list until a suitable donor kidney becomes available for transplantation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "819100e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patient_demographics_data = pd.read_csv(\n",
+    "    'https://repo.hops.works/dev/davit/hospital_wait_time/patient_demographics.csv', \n",
+    "    parse_dates=['date'],\n",
+    ")\n",
+    "patient_demographics_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d827df9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "medical_background_data = pd.read_csv(\n",
+    "    'https://repo.hops.works/dev/davit/hospital_wait_time/medical_background.csv', \n",
+    "    parse_dates=['date'],\n",
+    ")\n",
+    "medical_background_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f7973d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transplant_compatibility_data = pd.read_csv(\n",
+    "    'https://repo.hops.works/dev/davit/hospital_wait_time/transplant_compatibility.csv', \n",
+    "    parse_dates=['date'],\n",
+    ")\n",
+    "transplant_compatibility_data.columns = transplant_compatibility_data.columns.str.lower()\n",
+    "transplant_compatibility_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a4c7c6",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 👨🏻‍🍳 Data Preparation</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23284e32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patient_demographics_data.isna().sum()[patient_demographics_data.isna().sum() > 0] / len(patient_demographics_data)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5532bdbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "medical_background_data.isna().sum()[medical_background_data.isna().sum() > 0] / len(medical_background_data)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31876981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transplant_compatibility_data.isna().sum()[transplant_compatibility_data.isna().sum() > 0] / len(transplant_compatibility_data)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "058cf4f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "medical_background_data['dialysis_duration'] = medical_background_data['dialysis_duration'].fillna(1).replace(0, 1)\n",
+    "medical_background_data['dialysis_duration'] = np.log(medical_background_data['dialysis_duration'] + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6a1faef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_outliers_iqr(dataframe, iqr_multiplier=1.5):\n",
+    "    # Select numerical columns for outlier removal\n",
+    "    numerical_columns = dataframe.select_dtypes(\n",
+    "        include=['int64', 'float64']).columns\n",
+    "\n",
+    "    # Loop through numerical columns to identify and remove outliers using IQR\n",
+    "    for column in numerical_columns:\n",
+    "        Q1 = dataframe[column].quantile(0.25)\n",
+    "        Q3 = dataframe[column].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "        lower_bound = Q1 - iqr_multiplier * IQR\n",
+    "        upper_bound = Q3 + iqr_multiplier * IQR\n",
+    "\n",
+    "        outliers = dataframe[(dataframe[column] < lower_bound) | (\n",
+    "            dataframe[column] > upper_bound)]\n",
+    "\n",
+    "        # Remove outliers\n",
+    "        dataframe = dataframe[~dataframe.index.isin(outliers.index)]\n",
+    "\n",
+    "    return dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cacfd97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patient_demographics_data_filtered = remove_outliers_iqr(patient_demographics_data, iqr_multiplier=1.5)\n",
+    "print(f'⛳️ Original shape: {patient_demographics_data.shape}')\n",
+    "print(f'⛳️ Cleared shape: {patient_demographics_data_filtered.shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de3c3316",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "medical_background_data_filtered = remove_outliers_iqr(medical_background_data, iqr_multiplier=1.5)\n",
+    "print(f'⛳️ Original shape: {medical_background_data.shape}')\n",
+    "print(f'⛳️ Cleared shape: {medical_background_data_filtered.shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45b9c45b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transplant_compatibility_data_filtered = remove_outliers_iqr(transplant_compatibility_data, iqr_multiplier=1.5)\n",
+    "print(f'⛳️ Original shape: {transplant_compatibility_data.shape}')\n",
+    "print(f'⛳️ Cleared shape: {transplant_compatibility_data_filtered.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f7ac6f7",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\">👮🏻‍♂️ Great Expectations </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f99a5cff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import great_expectations as ge\n",
+    "from great_expectations.core import ExpectationSuite, ExpectationConfiguration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee28579a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert your DataFrame to a Great Expectations DataFrame\n",
+    "ge_df_patient_demographics = ge.from_pandas(patient_demographics_data_filtered)\n",
+    "\n",
+    "# Retrieve the expectation suite associated with the ge DataFrame\n",
+    "expectation_suite_patient_demographics = ge_df_patient_demographics.get_expectation_suite()\n",
+    "\n",
+    "# Set the expectation suite name\n",
+    "expectation_suite_patient_demographics.expectation_suite_name = \"patient_registration_suite\"\n",
+    "\n",
+    "# Expectation: 'id' should always be unique and not null\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_unique\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_not_be_null\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation: 'date' should be a valid date and not null\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_of_type\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"date\",\n",
+    "            \"type_\": \"datetime64[ns]\",\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_not_be_null\",\n",
+    "        kwargs={\"column\": \"date\"},\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation: 'age_at_list_registration' to be non-negative\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_between\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"age_at_list_registration\",\n",
+    "            \"min_value\": 0,\n",
+    "            \"max_value\": None,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation: 'gender' to be within expected values\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_in_set\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"gender\",\n",
+    "            \"value_set\": [\"M\", \"F\"],\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation: 'age_cat' to contain expected categories\n",
+    "expectation_suite_patient_demographics.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_in_set\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"age_cat\",\n",
+    "            \"value_set\": [\"Over60\", \"From18to60\", \"Below18\"],\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\"✅ Expectations defined and saved successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "640bbf2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ge_df_medical_background = ge.from_pandas(medical_background_data_filtered)\n",
+    "\n",
+    "# Retrieve and set the expectation suite\n",
+    "expectation_suite_medical_background = ge_df_medical_background.get_expectation_suite()\n",
+    "expectation_suite_medical_background.expectation_suite_name = \"medical_background_suite\"\n",
+    "\n",
+    "# Expectations for 'id' and 'date'\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_unique\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_not_be_null\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_of_type\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"date\",\n",
+    "            \"type_\": \"datetime64[ns]\",\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_not_be_null\",\n",
+    "        kwargs={\"column\": \"date\"},\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation for 'dialysis_duration'\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_between\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"dialysis_duration\",\n",
+    "            \"min_value\": 0,\n",
+    "            \"max_value\": None,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation for 'blood_gp'\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_in_set\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"blood_gp\",\n",
+    "            \"value_set\": [\"A\", \"B\", \"AB\", \"O\"],\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Gestation and Prior Transplant Expectations\n",
+    "for column in [\"gestation\", \"prior_transplant\"]:\n",
+    "    expectation_suite_medical_background.add_expectation(\n",
+    "        ExpectationConfiguration(\n",
+    "            expectation_type=\"expect_column_values_to_be_in_set\",\n",
+    "            kwargs={\n",
+    "                \"column\": column,\n",
+    "                \"value_set\": [\"YES\", \"NO\"],\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Expectation for 'number_prior_transplant' - check alignment with 'prior_transplant'\n",
+    "expectation_suite_medical_background.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_between\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"number_prior_transplant\",\n",
+    "            \"min_value\": 0,\n",
+    "            \"max_value\": None,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\"✅ Expectations defined and saved successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ca9a557",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ge_df_transplant_compatibility = ge.from_pandas(transplant_compatibility_data_filtered)\n",
+    "\n",
+    "# Retrieve and set the expectation suite\n",
+    "expectation_suite_transplant_compatibility = ge_df_transplant_compatibility.get_expectation_suite()\n",
+    "expectation_suite_transplant_compatibility.expectation_suite_name = \"transplant_compatibility_and_outcome_suite\"\n",
+    "\n",
+    "# Expectations for 'id' and 'date'\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_unique\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_not_be_null\",\n",
+    "        kwargs={\"column\": \"id\"},\n",
+    "    )\n",
+    ")\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_of_type\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"date\",\n",
+    "            \"type_\": \"datetime64[ns]\",\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation for 'cPRA' to be between 0 and 100\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_between\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"cpra\",\n",
+    "            \"min_value\": 0,\n",
+    "            \"max_value\": 100,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# HLA Marker Expectations (checking they are non-negative integers)\n",
+    "for hla_marker in [\"hla_a1\", \"hla_a2\", \"hla_b1\", \"hla_b2\", \"hla_dr1\", \"hla_dr2\"]:\n",
+    "    expectation_suite_transplant_compatibility.add_expectation(\n",
+    "        ExpectationConfiguration(\n",
+    "            expectation_type=\"expect_column_values_to_be_of_type\",\n",
+    "            kwargs={\n",
+    "                \"column\": hla_marker,\n",
+    "                \"type_\": \"int\",\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Expectation for 'if_transplanted'\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_in_set\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"if_transplanted\",\n",
+    "            \"value_set\": [\"YES\", \"NO\"],\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Expectation for 'duration' to be non-negative\n",
+    "expectation_suite_transplant_compatibility.add_expectation(\n",
+    "    ExpectationConfiguration(\n",
+    "        expectation_type=\"expect_column_values_to_be_between\",\n",
+    "        kwargs={\n",
+    "            \"column\": \"duration\",\n",
+    "            \"min_value\": 0,\n",
+    "            \"max_value\": None,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\"✅ Expectations defined and saved successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed8fa713",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 📡 Connecting to Hopsworks Feature Store </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82777378",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hopsworks\n",
+    "\n",
+    "project = hopsworks.login()\n",
+    "\n",
+    "fs = project.get_feature_store()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff82304a",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 🪄 Creating Feature Groups </span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d21bff59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get or create the 'patient_info' feature group\n",
+    "patient_info_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"patient_info\",\n",
+    "    version=1,\n",
+    "    description=\"Demographic Features\",\n",
+    "    primary_key=[\"id\"],\n",
+    "    event_time=\"date\",\n",
+    "    expectation_suite=expectation_suite_patient_demographics,\n",
+    ")\n",
+    "\n",
+    "patient_info_fg.insert(patient_demographics_data_filtered)\n",
+    "print('✅ Done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a524f361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get or create the 'medical_info' feature group\n",
+    "medical_info_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"medical_info\",\n",
+    "    version=1,\n",
+    "    description=\"Medical background features\",\n",
+    "    primary_key=[\"id\"],\n",
+    "    event_time=\"date\",\n",
+    "    expectation_suite=expectation_suite_medical_background,\n",
+    ")\n",
+    "\n",
+    "medical_info_fg.insert(medical_background_data_filtered)\n",
+    "print('✅ Done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f476f24b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get or create the 'transplant_compatibility' feature group\n",
+    "transplant_compatibility_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"transplant_compatibility\",\n",
+    "    version=1,\n",
+    "    description=\"Transplant compatibility features\",\n",
+    "    primary_key=[\"id\"],\n",
+    "    event_time=\"date\",\n",
+    "    expectation_suite=expectation_suite_transplant_compatibility,\n",
+    ")\n",
+    "\n",
+    "transplant_compatibility_fg.insert(transplant_compatibility_data_filtered)\n",
+    "print('✅ Done')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e15c005",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.1.-1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/advanced_tutorials/hospital_wait_time/2_training_pipeline.ipynb b/advanced_tutorials/hospital_wait_time/2_training_pipeline.ipynb
new file mode 100644
index 00000000..e8583a6a
--- /dev/null
+++ b/advanced_tutorials/hospital_wait_time/2_training_pipeline.ipynb
@@ -0,0 +1,537 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b6699fe1",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#ff5f27'> 📝 Imports </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a39a3d61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "import os\n",
+    "import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from prophet import Prophet\n",
+    "from prophet.serialize import model_to_json\n",
+    "\n",
+    "# Mute warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f80e358",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 📡 Connecting to Hopsworks Feature Store </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f88a3faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hopsworks\n",
+    "\n",
+    "project = hopsworks.login()\n",
+    "\n",
+    "fs = project.get_feature_store()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d65e85bf",
+   "metadata": {},
+   "source": [
+    "### <span style=\"color:#ff5f27;\"> 🔪 Feature Selection </span>\n",
+    "\n",
+    "You will start by selecting all the features you want to include for model training/inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a11ec70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the 'patient_info' feature group\n",
+    "patient_info_fg = fs.get_feature_group(\n",
+    "    name=\"patient_info\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "# Retrieve the 'medical_info' feature group\n",
+    "medical_info_fg = fs.get_feature_group(\n",
+    "    name=\"medical_info\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "# Retrieve the 'transplant_compatibility' feature group\n",
+    "transplant_compatibility_fg = fs.get_feature_group(\n",
+    "    name=\"transplant_compatibility\",\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17ba77ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select features for training data.\n",
+    "selected_features = patient_info_fg.select_all([\"id\", \"date\"])\\\n",
+    "    .join(medical_info_fg.select_except([\"id\", \"date\"]))\\\n",
+    "    .join(transplant_compatibility_fg.select_except([\"id\", \"date\"])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "495cbb5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment this if you would like to view your selected features\n",
+    "selected_features.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5902c9e",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> ⚙️ Transformation Functions </span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba4ae19c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[f.name for f in fs.get_transformation_functions()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c32555b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_encoder = fs.get_transformation_function(name=\"label_encoder\")\n",
+    "\n",
+    "standard_scaler = fs.get_transformation_function(name=\"standard_scaler\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4580c73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_category = ['gender', 'age_cat', 'blood_gp', 'underlying_disease', 'gestation', 'prior_transplant', 'if_transplanted']\n",
+    "\n",
+    "transformation_functions_category = {\n",
+    "    feature_name: label_encoder\n",
+    "    for feature_name\n",
+    "    in features_category\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be5eb3c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_numerical = [\n",
+    "    'age_at_list_registration', 'dialysis_duration', 'number_prior_transplant', 'cpra', 'hla_a1', 'hla_a2', 'hla_b1', 'hla_b2', 'hla_dr1', 'hla_dr2',\n",
+    "]\n",
+    "\n",
+    "transformation_functions_numerical = {\n",
+    "    feature_name: standard_scaler\n",
+    "    for feature_name\n",
+    "    in features_numerical\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a7e76ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Join transformation_functions_category and transformation_functions_numerical dictionaries into one\n",
+    "transformation_functions = transformation_functions_category | transformation_functions_numerical"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91636dc3",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> ⚙️ Feature View Creation </span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "576617c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get or create the 'medical_features' feature view\n",
+    "feature_view = fs.get_or_create_feature_view(\n",
+    "    name='medical_features',\n",
+    "    version=1,\n",
+    "    query=selected_features,\n",
+    "    labels=[\"duration\"],\n",
+    "    transformation_functions=transformation_functions,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3bb3b8e",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 🏋️ Training Dataset Creation</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7851e335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split date with percentage \n",
+    "df = patient_info_fg.read()\n",
+    "\n",
+    "def split_dfs(df): \n",
+    "    df = df.sort_values(by='date') \n",
+    "    trainvals = df[:int(len(df)*0.8)] \n",
+    "    testvals = df[int(len(df)*0.8):] \n",
+    "    return {\n",
+    "        'train_start': min(trainvals.date).date(), \n",
+    "        'train_end': max(trainvals.date).date(), \n",
+    "        'test_start': min(testvals.date).date(), \n",
+    "        'test_end': max(testvals.date).date(),\n",
+    "    }\n",
+    "\n",
+    "split_dict = split_dfs(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7a8f6f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69f4373c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = feature_view.train_test_split(\n",
+    "    train_start=split_dict['train_start'],\n",
+    "    train_end=split_dict['train_end'],\n",
+    "    test_start=split_dict['test_start'],\n",
+    "    test_end=split_dict['test_end'],    \n",
+    "    event_time=True,\n",
+    ")\n",
+    "X_train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2facefa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d510db36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort the X_train DataFrame based on the \"datetime\" column in ascending order\n",
+    "X_train = X_train.sort_values(\"date\")\n",
+    "# Reindex the y_train Series to match the order of rows in the sorted X_train DataFrame\n",
+    "y_train = y_train.reindex(X_train.index)\n",
+    "\n",
+    "# Sort the X_test DataFrame based on the \"datetime\" column in ascending order\n",
+    "X_test = X_test.sort_values(\"date\")\n",
+    "# Reindex the y_test Series to match the order of rows in the sorted X_test DataFrame\n",
+    "y_test = y_test.reindex(X_test.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7e10eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train['y'] = y_train\n",
+    "X_train['ds'] = X_train.date\n",
+    "X_train['ds'] = pd.to_datetime(X_train.ds)\n",
+    "X_train['ds'] = X_train.ds.map(lambda x: x.replace(tzinfo=None))\n",
+    "X_train.drop(columns=[\"date\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfbb7b31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test['ds'] = X_test.date\n",
+    "X_test['ds'] = pd.to_datetime(X_test.ds)\n",
+    "X_test['ds'] = X_test.ds.map(lambda x: x.replace(tzinfo=None))\n",
+    "X_test.drop(columns=[\"date\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3847431e",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 🧬 Modeling</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d639b394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the Prophet model with the appropriate seasonalities\n",
+    "model = Prophet(\n",
+    "    daily_seasonality=False,\n",
+    "    weekly_seasonality=True,\n",
+    "    yearly_seasonality=True,\n",
+    ")\n",
+    "\n",
+    "# Add monthly seasonality with a period of 30.5 days (average length of a month)\n",
+    "model.add_seasonality(\n",
+    "    name='monthly', \n",
+    "    period=30.5, \n",
+    "    fourier_order=5,\n",
+    "    mode='additive',\n",
+    ")\n",
+    "\n",
+    "# Add the additional regressors\n",
+    "additional_regressors = [\n",
+    "    'age_at_list_registration','cpra', 'hla_a1', 'hla_a2', 'hla_b1', 'hla_b2', 'hla_dr1', 'hla_dr2',\n",
+    "]\n",
+    "\n",
+    "for regressor in additional_regressors:\n",
+    "    model.add_regressor(regressor)\n",
+    "\n",
+    "# Fit the model\n",
+    "model.fit(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce527621",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forecast = model.predict(X_test)\n",
+    "\n",
+    "# Summarize the forecast\n",
+    "print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head())\n",
+    "\n",
+    "# Plot the forecast\n",
+    "fig = model.plot(forecast)\n",
+    "\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e4217c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.plot_components(forecast)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8701339",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate MAE between expected and predicted values for december\n",
+    "y_pred = forecast['yhat']\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "print('MAE: %.3f' % mae)\n",
+    "# plot expected vs actual\n",
+    "\n",
+    "metrics = {\n",
+    "    \"mae\": round(mae,2)\n",
+    "}\n",
+    "metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea6cfd10",
+   "metadata": {},
+   "source": [
+    "### <span style=\"color:#ff5f27;\">⚙️ Model Schema</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bf4bb02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hsml.schema import Schema\n",
+    "from hsml.model_schema import ModelSchema\n",
+    "\n",
+    "# Define the input schema using the values of X_test\n",
+    "input_schema = Schema(X_test.values)\n",
+    "\n",
+    "# Define the output schema using y_train\n",
+    "output_schema = Schema(y_train)\n",
+    "\n",
+    "# Create a ModelSchema object specifying the input and output schemas\n",
+    "model_schema = ModelSchema(\n",
+    "    input_schema=input_schema, \n",
+    "    output_schema=output_schema,\n",
+    ")\n",
+    "\n",
+    "# Convert the model schema to a dictionary for further inspection or serialization\n",
+    "model_schema.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a92ddd",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\">📝 Register model</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dbc8bae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the directory where the model will be saved\n",
+    "model_dir = \"forecast_model\"\n",
+    "\n",
+    "# Check if the directory exists, and create it if it doesn't\n",
+    "if not os.path.isdir(model_dir):\n",
+    "    os.mkdir(model_dir)\n",
+    "\n",
+    "# Save the trained model using joblib\n",
+    "with open(model_dir + '/serialized_model.json', 'w') as fout:\n",
+    "    fout.write(model_to_json(model))  # Save model\n",
+    "    \n",
+    "# Save the confusion matrix plot as an image file in the 'iris_model' directory\n",
+    "fig.savefig(model_dir + \"/forecast.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "707022f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the model registry\n",
+    "mr = project.get_model_registry()\n",
+    "\n",
+    "# Create a new model in the model registry\n",
+    "forecast_model = mr.python.create_model(\n",
+    "    name=\"waiting_time_forecast_model\",   # Name for the model\n",
+    "    metrics=metrics,                      # Metrics used for evaluation\n",
+    "    model_schema=model_schema,            # Schema defining the model's input and output\n",
+    "    input_example=X_test.sample(),        # Example input data for reference\n",
+    "    description=\"Waiting time for a deceased donor kidney transplant forecasting model\",  # Description of the model\n",
+    ")\n",
+    "\n",
+    "# Save the model to the specified directory\n",
+    "forecast_model.save(model_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0db0bf98",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/advanced_tutorials/hospital_wait_time/3_inference_pipeline.ipynb b/advanced_tutorials/hospital_wait_time/3_inference_pipeline.ipynb
new file mode 100644
index 00000000..af472ff3
--- /dev/null
+++ b/advanced_tutorials/hospital_wait_time/3_inference_pipeline.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72f42eba",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#ff5f27'> 📝 Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8158dcec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "from prophet.serialize import model_from_json\n",
+    "from matplotlib import pyplot\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faa87ecf",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> 📡 Connecting to Hopsworks Feature Store </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4330a4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hopsworks\n",
+    "\n",
+    "project = hopsworks.login()\n",
+    "\n",
+    "fs = project.get_feature_store()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dff5ebe0",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\"> ⚙️ Feature View Retrieval</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd3f8c1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the 'medical_features' feature view\n",
+    "feature_view = fs.get_feature_view(\n",
+    "    name='medical_features',\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b6f351",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\">🗄 Model Registry</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecad81cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the model registry\n",
+    "mr = project.get_model_registry()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "515fe05a",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#ff5f27'>🚀 Fetch and test the model</span>\n",
+    "\n",
+    "Finally you can start making predictions with your model!\n",
+    "\n",
+    "Retrieve your model from Hopsworks model registry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbb5cdc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the model from the model registry\n",
+    "retrieved_model = mr.get_model(\n",
+    "    name=\"waiting_time_forecast_model\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "# Download the saved model files to a local directory\n",
+    "saved_model_dir = retrieved_model.download()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dfaba98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(saved_model_dir + '/serialized_model.json', 'r') as fin:\n",
+    "    model = model_from_json(fin.read())  # Load model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e5b40c5",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27;\">🔮  Batch Prediction </span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5e49a6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize batch scoring\n",
+    "feature_view.init_batch_scoring(1)\n",
+    "\n",
+    "# Get the batch data\n",
+    "batch_data = feature_view.get_batch_data(\n",
+    "    start_time=datetime(2015, 10, 19), \n",
+    "    end_time=datetime(2017, 12, 29), \n",
+    "    event_time=True,\n",
+    ")\n",
+    "\n",
+    "batch_data['ds'] = batch_data.date\n",
+    "batch_data['ds'] = pd.to_datetime(batch_data.ds)\n",
+    "batch_data['ds'] = batch_data.ds.map(lambda x: x.replace(tzinfo=None))\n",
+    "batch_data.drop(columns=[\"date\"], axis=1, inplace=True)\n",
+    "batch_data = batch_data.sort_values(\"ds\")\n",
+    "\n",
+    "# Display the first 3 rows\n",
+    "batch_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ff70533",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the model to make a forecast\n",
+    "forecast = model.predict(batch_data)\n",
+    "\n",
+    "# summarize the forecast\n",
+    "print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6821c72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.plot(forecast)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c6c4a04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.plot_components(forecast)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "583f95e2",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/advanced_tutorials/hospital_wait_time/requirements.txt b/advanced_tutorials/hospital_wait_time/requirements.txt
new file mode 100644
index 00000000..3f5db197
--- /dev/null
+++ b/advanced_tutorials/hospital_wait_time/requirements.txt
@@ -0,0 +1 @@
+prophet==1.1.5