diff --git a/sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb b/sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb
index 4904fbbfe7..c49389ab19 100644
--- a/sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb
+++ b/sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0c9ee48f",
+   "id": "91ee6b6d",
    "metadata": {},
    "source": [
     "# Creating an Amazon Forecast Predictor with SageMaker Pipelines\n",
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "932dc4d0",
+   "id": "86a4678e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de12d5a8",
+   "id": "b0125efc",
    "metadata": {},
    "source": [
     "Finally, you will need the following trust policies."
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "957d4d1f",
+   "id": "8050bbca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45298f90",
+   "id": "9ed30cce",
    "metadata": {},
    "source": [
     "## Prerequisites\n",
@@ -95,7 +95,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ab8df52",
+   "id": "1d137518",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install sagemaker==2.93.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b763c7b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51f2beea",
+   "id": "ad1e02a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fff1a8b5",
+   "id": "23cb4c19",
    "metadata": {},
    "source": [
     "## Dataset\n",
@@ -200,7 +210,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4367c4a",
+   "id": "d71ab7f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +236,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a4a1d4a",
+   "id": "8ce30a2a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db24153a",
+   "id": "f58aea79",
    "metadata": {},
    "source": [
     "The dataset happens to span January 01, 2011, to January 01, 2015. We are only going to use about two and a half week's of hourly data to train Amazon Forecast. \n",
@@ -253,7 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ac099d3c",
+   "id": "724eee5f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d114bd69",
+   "id": "18b87844",
    "metadata": {},
    "source": [
     "Next, we define parameters that can be set for the execution of the pipeline. They serve as variables. We define the following:\n",
@@ -286,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1d86c83",
+   "id": "52ba0c45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +304,6 @@
     "processing_instance_type = ParameterString(\n",
     "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.large\"\n",
     ")\n",
-    "training_instance_count = ParameterInteger(name=\"TrainingInstanceCount\", default_value=1)\n",
     "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.large\")\n",
     "\n",
     "input_train = ParameterString(\n",
@@ -312,7 +321,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eff2dad9",
+   "id": "3a2ee68c",
    "metadata": {},
    "source": [
     "We use an updated [SKLearnProcessor](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html#sagemaker.sklearn.processing.SKLearnProcessor) to run Python scripts to build a dataset group and train an Amazon Forecast predictor using `boto3`. In the next chunk, we instantiate an instance of `ScriptProcessor`, which is essentially an SKLearnProcessor with updated `boto3` and `botocore` (as built above) that we use in the next steps. "
@@ -321,7 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11e82c55",
+   "id": "130c2059",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +345,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88fb293a",
+   "id": "2abf7b80",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,7 +362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d40d2b1",
+   "id": "26bd50c0",
    "metadata": {},
    "source": [
     "First we preprocess the data using an Amazon SageMaker [ProcessingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html?highlight=ProcessingStep#sagemaker.workflow.steps.ProcessingStep) that provides a containerized execution environment to run the `preprocess.py` script."
@@ -362,7 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b5d84ca3",
+   "id": "aa0259f4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d4b1540",
+   "id": "6e05150d",
    "metadata": {},
    "source": [
     "The next step is to train and evaluate the forecasting model calling Amazon Forecast using `boto3`. We instantiate an instance of `SKLearn` estimator that we use in the next `TrainingStep` to run the script `train.py`. \n",
@@ -394,7 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b80ada3f",
+   "id": "95177b2f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +434,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b52d154a",
+   "id": "cf10e258",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -433,7 +442,6 @@
     "    entry_point=\"train.py\",\n",
     "    role=role_arn,\n",
     "    image_uri=container_image_uri,\n",
-    "    instance_count=training_instance_count,\n",
     "    instance_type=training_instance_type,\n",
     "    sagemaker_session=sagemaker_session,\n",
     "    base_job_name=\"forecast-train\",\n",
@@ -446,7 +454,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f5ddecce",
+   "id": "82f5536a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29f0d4d4",
+   "id": "867f2daf",
    "metadata": {},
    "source": [
     "The third step is an Amazon SageMaker ProcessingStep that deletes or keeps the Amazon Forecast model running using the script `conditional_delete.py`. If the error reported after training is higher than a threshold you specify for the metric you specify, this step deletes all the resources created by Amazon Forecast that are related to the pipeline's execution.\n",
@@ -465,7 +473,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43c79816",
+   "id": "f6122249",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +500,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41ef4915",
+   "id": "991697b7",
    "metadata": {},
    "source": [
     "Finally, we combine all the steps and define our pipeline."
@@ -501,7 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cf7b196",
+   "id": "fdc925a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +521,6 @@
     "    parameters=[\n",
     "        processing_instance_type,\n",
     "        processing_instance_count,\n",
-    "        training_instance_count,\n",
     "        training_instance_type,\n",
     "        input_train,\n",
     "        forecast_horizon,\n",
@@ -532,7 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c838b490",
+   "id": "681b8721",
    "metadata": {},
    "source": [
     "Once the pipeline is successfully defined, we can start the execution."
@@ -541,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1cbe62f1",
+   "id": "5b375f45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -551,7 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35e9c22d",
+   "id": "b2fec897",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,7 +568,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ccb70b34",
+   "id": "72464cc3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,7 +578,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a20d8f39",
+   "id": "e66f34a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,7 +587,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e285dfd",
+   "id": "c5c56dff",
    "metadata": {},
    "source": [
     "## Experiments Tracking\n",
@@ -602,7 +609,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "067b7888",
+   "id": "a0030897",
    "metadata": {},
    "source": [
     "## Conclusion"
@@ -610,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40a6ba7e",
+   "id": "132ad067",
    "metadata": {},
    "source": [
     "In this notebook we have seen how to create a SageMaker Pipeline to train an Amazon Forecast predictor on your own dataset with a target and related time series."
@@ -618,7 +625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93c99720",
+   "id": "d10d8baf",
    "metadata": {},
    "source": [
     "## Clean up\n",
@@ -629,7 +636,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc956081",
+   "id": "c8665320",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +661,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4c2e6e8a",
+   "id": "6a234cbf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -670,7 +677,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e50ca583",
+   "id": "b269f192",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -680,7 +687,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "82e5928a",
+   "id": "a8828ef6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -690,7 +697,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44212447",
+   "id": "abeda944",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -708,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41649cfd",
+   "id": "d64d4ae5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a9fd48dc",
+   "id": "33d2861f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -733,7 +740,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cc00b557",
+   "id": "d0e4bd9d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +751,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "336300e0",
+   "id": "d9968b15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -756,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8eed39f2",
+   "id": "1440c84e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -767,9 +774,9 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "conda_python3",
    "language": "python",
-   "name": "python3"
+   "name": "conda_python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -781,7 +788,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,