Machine Learning capabilities in Azure Synapse Analytics

Machine Learning capabilities in Azure Synapse Analytics

Numerous Machine Learning features are available with Azure Synapse Analytics. Various machine learning capabilities in Azure Synapse Analytics are used to help in the data science process.

Gathering and interpreting data

Accessing and comprehending the data is one of the main steps in the majority of machine learning initiatives. Various machine learning capabilities in Azure Synapse Analytics are used to gather and interpret the data.

Source: Productive Edge

Data source and pipelines

Azure Data Factory is a native component of the azure synapse, and it contains a substantial collection of tools available for data intake and management pipelines. As a result, building data pipelines to access and transform the data into a format suitable for machine learning is made simple and is one of the significant machine learning capabilities in Azure Synapse Analytics.

Data visualization

It is essential to understand the data by visualization. Synapse provides various tools for data exploration and preparation for analytics and machine learning. Apache Spark is one of the simplest methods to begin data exploration. Your data can be transformed, prepared, and explored at scale using Apache Spark for Azure Synapse. These spark pools include technologies like PySpark/Python, Scala, and.NET for large-scale data processing. The data exploration experience may be improved to aid in better understanding the data by utilizing complete visualization packages. 

Source: BoostLabs

Modeling

Machine learning models can be trained on the Apache Spark Pools with tools such as PySpark, Scala, or .NET. There are various machine learning capabilities in Azure Synapse Analytics that are used for training the models.

Train models on Spark Pools with MLlib

Various techniques and libraries can be used to train machine learning models. Scalable machine learning techniques are provided by Spark MLlib. It can assist in resolving the majority of traditional machine learning issues. Models may be created using MLlib as well as with well-known tools like Scikit Learn. It is one of the significant machine learning capabilities in Azure Synapse Analytics.

Train models with Azure Machine Learning automated ML

Automated machine learning is a feature that trains a variety of machine learning models automatically and lets the user choose the best model based on predetermined metrics. Users may simply make use of automatic ML in Synapse with passthrough Azure Active Directory authentication thanks to a smooth interface with Azure Machine Learning from Azure Synapse Notebooks.

Model deployment and scoring

Models trained in Azure Synapse or outside of Azure Synapse may be simply utilized for batch scoring. Machine learning capabilities in Azure Synapse Analytics offers two ways to conduct batch scoring.

To perform your predictions directly where your data resides, we can use the TSQL PREDICT function in Synapse SQL pools. We can enhance our data with the TSQL PREDICT function without removing any data from our data warehouse. We can install an ONNX model from the Azure Machine Learning model registry in Synapse SQL Pools for batch scoring using PREDICT.

Azure Spark Pools can be used for batch-scoring machine learning models in Azure Synapse. Depending on the libraries used to train the models, we can use a code interface to execute our batch scoring.

Must Read Apache Server

Frequently Asked Questions

What is Azure Resource Manager?

The service that delivers and maintains Azure resources is called Azure Resource Manager. You can add, update, and remove Azure account resources using its management layer.

What is a data pipeline?

A data pipeline is an automated method for moving and transforming data between a source system and a target repository.

What is meant by TSQL?

Transact SQL or TSQL is a query language used only by the Microsoft SQL Server software. It can assist with tasks like getting data from a single row, adding additional rows, and getting data from several rows.

Conclusion

In this blog, we talked about the various machine learning capabilities in Azure Synapse Analytics.

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