Update apache-spark-machine-learning-training.md

Removed Automated ML in Azure Machine Learning (deprecated), which is based on Azure for Apache Spark 2.4 which has been deprecated and disabled

Author: jeacla

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

@@ -37,20 +37,6 @@ The Microsoft Machine Learning library for Apache Spark is [MMLSpark](https://gi
 
 MMLSpark provides a layer on top of SparkML's low-level APIs when building scalable ML models, such as indexing strings, coercing data into a layout expected by machine learning algorithms, and assembling feature vectors. The MMLSpark library simplifies these and other common tasks for building models in PySpark.
 
-## Automated ML in Azure Machine Learning (deprecated)
-Azure Machine Learning is a cloud-based environment that allows you to train, deploy, automate, manage, and track machine learning models. Automated ML in Azure Machine Learning accepts training data and configuration settings and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model. 
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-When using automated ML within Azure Synapse Analytics, you can leverage the deep integration between the different services to simplify authentication & model training. 
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-> [!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).
->
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-<a name='azure-cognitive-services'></a>
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 ## Azure AI services
 [Azure AI services](/azure/ai-services/what-are-ai-services) provides machine learning capabilities to solve general problems such as analyzing text for emotional sentiment or analyzing images to recognize objects or faces. You don't need special machine learning or data science knowledge to use these services. A Cognitive Service provides part or all of the components in a machine learning solution: data, algorithm, and trained model. These services are meant to require general knowledge about your data without needing experience with machine learning or data science. You can leverage these pre-trained Azure AI services automatically within Azure Synapse Analytics.