Machine Learning capabilities in Azure Synapse Analytics

Azure Synapse Analytics offers various machine learning capabilities. This article provides an overview of how you can apply Machine Learning in the context of Azure Synapse.

This overview covers the different capabilities in Synapse related to machine learning, from a data science process perspective.

You may be familiar with how a typical data science process looks. It's a well-known process, which most machine learning projects follow.

At a high level, the process contains the following steps:

  • Business understanding (not discussed in this article)
  • Data acquisition and understanding
  • Modeling
  • Model deployment and scoring

This article describes the Azure Synapse machine learning capabilities in different analytics engines, from a data science process perspective. For each step in the data science process, the Azure Synapse capabilities that can help are summarized.

Data acquisition and understanding

Most machine learning projects involve well-established steps, and one of these steps is to access and understand the data.

Data source and pipelines

Thanks to Azure Data Factory, a natively integrated part of Azure Synapse, there is a powerful set of tools available for data ingestion and data orchestration pipelines. This allows you to easily build data pipelines to access and transform the data into a format that can be consumed for machine learning. Learn more about data pipelines in Synapse.

Data preparation and exploration/visualization

An important part of the machine learning process is to understand the data by exploration and visualizations.

Depending on where the data is stored, Synapse offers a set of different tools to explore and prepare it for analytics and machine learning. One of the quickest ways to get started with data exploration is using Apache Spark or serverless SQL pools directly over data in the data lake.


In Azure Synapse, training machine learning models can be performed on the Apache Spark Pools with tools like PySpark/Python, Scala, or .NET.

Train models on Spark Pools with MLlib

Machine learning models can be trained with help from various algorithms and libraries. Spark MLlib offers scalable machine learning algorithms that can help solving most classical machine learning problems. For a tutorial on how to train a model using MLlib in Synapse, see Build a machine learning app with Apache Spark MLlib and Azure Synapse Analytics.

In addition to MLlib, popular libraries such as Scikit Learn can also be used to develop models. See Manage libraries for Apache Spark in Azure Synapse Analytics for details on how to install libraries on Synapse Spark Pools.

Train models with Azure Machine Learning automated ML

Another way to train machine learning models, that does not require much prior familiarity with machine learning, is to use automated ML. Automated ML is a feature that automatically trains a set of machine learning models and allows the user to select the best model based on specific metrics. Thanks to a seamless integration with Azure Machine Learning from Azure Synapse Notebooks, users can easily leverage automated ML in Synapse with passthrough Microsoft Entra authentication. This means that you only need to point to your Azure Machine Learning workspace and do not need to enter any credentials. The tutorial, Train a model in Python with automated machine learning, describes how to train models using Azure Machine Learning automated ML on Synapse Spark Pools.


  • Effective September 29, 2023, Azure Synapse will discontinue official support for Spark 2.4 Runtimes. 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.

Model deployment and scoring

Models that have been trained either in Azure Synapse or outside Azure Synapse can easily be used for batch scoring. Currently in Synapse, there are two ways in which you can run batch scoring.

  • You can use the TSQL PREDICT function in Synapse SQL pools to run your predictions right where your data lives. This powerful and scalable function allows you to enrich your data without moving any data out of your data warehouse. A new guided machine learning model experience in Synapse Studio was introduced where you can deploy an ONNX model from the Azure Machine Learning model registry in Synapse SQL Pools for batch scoring using PREDICT.

  • Another option for batch scoring machine learning models in Azure Synapse is to leverage the Apache Spark Pools for Azure Synapse. Depending on the libraries used to train the models, you can use a code experience to run your batch scoring.


SynapseML (previously known as MMLSpark), is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. It is an ecosystem of tools used to expand the Apache Spark framework in several new directions. SynapseML unifies several existing machine learning frameworks and new Microsoft algorithms into a single, scalable API that’s usable across Python, R, Scala, .NET, and Java. To learn more, see the key features of SynapseML.

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