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+# Automated Machine Learning using H2O in SQL Server 2019 Big Data Cluster
+
+## Machine Learning using Spark
+
+The new built-in notebooks in Azure Data Studio enable data scientists and data engineers to run Python, R, or Scala code against the cluster. This is a great way to explore the data and build machine learning models. Notebooks facilitate collaboration between teammates working on a shared data set.
+
+This sample uses the automated machine learning capabilities of the third party H2O package running in Spark in a SQL Server 2019 Big Data Cluster to build a machine learning model that predicts powerplant output.
+
+### Instructions
+
+1. From Azure Data Studio, connect to the SQL Server Big Data Cluster endpoint. Information about how you connect from Azure Data Studio can be found [here](https://docs.microsoft.com/en-us/sql/azure-data-studio/sql-server-2019-extension?view=sql-server-ver15).
+
+1. Download and save the notebook file [h2o-automl-powerplant.ipynb](h2o-automl-powerplant.ipynb/) locally.
+
+1. Open the notebook file in Azure Data Studio (right click on the SQL Server big data cluster server name-> **Manage**-> Open Notebook).
+
+1. Wait for the “Kernel” and the target context (“Attach to”) to be populated. Set the “Kernel” to **PySpark3** and “Attach to” needs to be the IP address of your big data cluster endpoint.
+
+1. Run each cell in the Notebook sequentially using Azure Data Studio. Expect the first cell to take 20 sec to finish. Other cells downloading and installing H2O, downloading data, and performing automated machine learning may take several minutes to finish.
diff --git a/samples/features/sql-big-data-cluster/machine-learning/spark/h2o/h2o-automl-powerplant.ipynb b/samples/features/sql-big-data-cluster/machine-learning/spark/h2o/h2o-automl-powerplant.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Powerplant Output Prediction\n",
+ "- This notebook is based on the power plant output prediction example presented in H2O’s [blog post on H2O AutoML in Spark](https://www.h2o.ai/blog/h2os-automl-in-spark/).\n",
+ "- Run this notebook in Azure Data Studio connected to a SQL Server 2019 Big Data Cluster by following the instructions [here](https://docs.microsoft.com/en-us/sql/big-data-cluster/notebooks-guidance?view=sqlallproducts-allversions)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Spark Configuration\n",
+ "- We can control the Spark Driver and Executor memory, cores, and number of executors per pod using the “%%configure” cell magic\n",
+ "- Additional configuration settings are listed at the end of this notebook\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "language": "python"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Current session configs: {'executorMemory': '4g', 'driverMemory': '4g', 'executorCores': 2, 'driverCores': 2, 'numExecutors': 2, 'kind': 'pyspark3'} "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "No active sessions."
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%configure -f\n",
+ "{\n",
+ " \"executorMemory\": \"4g\",\n",
+ " \"driverMemory\": \"4g\",\n",
+ " \"executorCores\": 2,\n",
+ " \"driverCores\": 2,\n",
+ " \"numExecutors\": 2\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Install H2O\n",
+ "- This cell downloads the h2o_pysparkling_2.3 python package and installs it on the pod where the Spark driver is currently running, if it is not already installed. Propagating the software to additional pods is handled automatically once we launch H2O.\n",
+ "- For an enterprise scenario where we cannot reach out to the PyPi repository on the Internet, pip3 can be pointed to a local copy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "language": "python"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Starting Spark application\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "