Merge pull request #268267 from midesa/main

AutoML Notice for Spark 2.4

Author: prmerger-automator[bot]

articles/synapse-analytics/machine-learning/quickstart-gallery-sample-notebook.md

@@ -13,9 +13,6 @@ ms.custom: mode-other
 # Quickstart: Use a sample notebook from the Synapse Analytics gallery
 
 In this quickstart, you'll learn how to copy a sample machine learning notebook from the Synapse Analytics gallery into your workspace, modify it, and run it.
-The sample notebook ingests an Open Dataset of NYC Taxi trips and uses visualization to help you prepare the data. It then trains a model to predict whether there will be a tip on a given trip.
-
-This notebook demonstrates the basic steps used in creating a model: **data import**, **data prep**, **model training**, and **evaluation**. You can use this sample as a starting point for creating a model with your own data.
 
 ## Prerequisites
 

articles/synapse-analytics/spark/apache-spark-azure-machine-learning-tutorial.md

@@ -6,10 +6,10 @@ ms.service: synapse-analytics
 ms.topic: tutorial
 ms.subservice: machine-learning
 ms.custom: devx-track-python
-ms.date: 06/30/2020
+ms.date: 03/06/2024
 ms.author: midesa
-ms.reviewer: sngun
 ---
+
 # Tutorial: Train a model in Python with automated machine learning
 
 Azure Machine Learning is a cloud-based environment that allows you to train, deploy, automate, manage, and track machine learning models. 
@@ -27,6 +27,12 @@ In this tutorial, you learn how to:
 - Create a serverless Apache Spark pool by following the [Create a serverless Apache Spark pool](../quickstart-create-apache-spark-pool-studio.md) quickstart.
 - Complete the [Azure Machine Learning workspace setup](../../machine-learning/quickstart-create-resources.md) tutorial if you don't have an existing Azure Machine Learning workspace. 
 
+> [!WARNING]
+> - Effective September 29, 2023, Azure Synapse will discontinue official support for [Spark 2.4 Runtimes](../spark/apache-spark-24-runtime.md). Post September 29, 2023, we will not be addressing any support tickets related to Spark 2.4. There will be no release pipeline in place for bug or security fixes for Spark 2.4. Utilizing Spark 2.4 post the support cutoff date is undertaken at one's own risk. We strongly discourage its continued use due to potential security and functionality concerns.
+> - As part of the deprecation process for Apache Spark 2.4, we would like to notify you that AutoML in Azure Synapse Analytics will also be deprecated. This includes both the low code interface and the APIs used to create AutoML trials through code.
+> - Please note that AutoML functionality was exclusively available through the Spark 2.4 runtime.
+> - For customers who wish to continue leveraging AutoML capabilities, we recommend saving your data into your Azure Data Lake Storage Gen2 (ADLSg2) account. From there, you can seamlessly access the AutoML experience through Azure Machine Learning (AzureML). Further information regarding this workaround is available [here](../machine-learning/access-data-from-aml.md).
+
 ## Understand regression models
 
 *Regression models* predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable affects the others.  

articles/synapse-analytics/spark/apache-spark-machine-learning-concept.md

@@ -5,9 +5,8 @@ author: midesa
 ms.service: synapse-analytics
 ms.topic: conceptual
 ms.subservice: machine-learning
-ms.date: 11/13/2020
+ms.date: 03/06/2023
 ms.author: midesa
-ms.reviewer: sngun
 ---
 
 # Machine learning with Apache Spark
@@ -40,6 +39,12 @@ Spark's in-memory distributed computation capabilities make it a good choice for
 ### Azure Machine Learning automated ML
 [Azure Machine Learning automated ML](../../machine-learning/concept-automated-ml.md) (automated machine learning) helps automate the process of developing machine learning models. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. The components to run the Azure Machine Learning automated ML SDK is built directly into the Synapse Runtime.
 
+> [!WARNING]
+> - Effective September 29, 2023, Azure Synapse will discontinue official support for [Spark 2.4 Runtimes](../spark/apache-spark-24-runtime.md). Post September 29, 2023, we will not be addressing any support tickets related to Spark 2.4. There will be no release pipeline in place for bug or security fixes for Spark 2.4. Utilizing Spark 2.4 post the support cutoff date is undertaken at one's own risk. We strongly discourage its continued use due to potential security and functionality concerns.
+> - As part of the deprecation process for Apache Spark 2.4, we would like to notify you that AutoML in Azure Synapse Analytics will also be deprecated. This includes both the low code interface and the APIs used to create AutoML trials through code.
+> - Please note that AutoML functionality was exclusively available through the Spark 2.4 runtime.
+> - For customers who wish to continue leveraging AutoML capabilities, we recommend saving your data into your Azure Data Lake Storage Gen2 (ADLSg2) account. From there, you can seamlessly access the AutoML experience through Azure Machine Learning (AzureML). Further information regarding this workaround is available [here](../machine-learning/access-data-from-aml.md).
+
 ### Open-source libraries
 Every Apache Spark pool in Azure Synapse Analytics comes with a set of pre-loaded and popular machine learning libraries.  Some of the relevant machine learning libraries that are included by default include: