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There will be times when you'll want to take an intermediate result from one experiment and use it as part of another experiment. To do this, you save the module as a dataset:
Learn how to upload a data file from your hard drive to use as training data in Azure Machine Learning Studio. By importing the data file, you have a dataset module ready for use in your workspace.
By using the [Import Data][import-data] module, you can access data from one of several online data sources while your experiment is running in [Azure Machine Learning Studio](https://studio.azureml.net/Home):
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# Import your training data into Azure Machine Learning Studio from various data sources
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To use your own data in Machine Learning Studio to develop and train a predictive analytics solution, you can:
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* upload data from a **local file** ahead of time from your hard drive to create a dataset module in your workspace
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* access data from one of several **online data sources** while your experiment is running using the [Import Data][import-data] module
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* use data from another Azure Machine learning **experiment** saved as a dataset
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* use data from an on-premises **SQL Server database**
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To use your own data in Machine Learning Studio to develop and train a predictive analytics solution, you can use data from:
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Each of these options is described in one of the topics on the menu below. These topics show you how to import data from these various data sources to use in Machine Learning Studio.
* A [**local file**](import-data-from-local-file.md) - Load local data ahead of time from your hard drive to create a dataset module in your workspace
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*[**Online data sources**](import-data-from-online-sources.md) - Use the [Import Data][import-data] module to access data from one of several online sources while your experiment is running
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*[**Machine Learning Studio experiment**](import-data-from-an-experiment.md) - Use data that was saved as a dataset in Machine Learning Studio
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*[**On-premises SQL Server database**](use-data-from-an-on-premises-sql-server.md) - Use data from an on-premises SQL Server database without having to copy data manually
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> [!NOTE]
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> There are a number of sample datasets available in Machine Learning Studio that you can use for training data. For information on these, see [Use the sample datasets in Azure Machine Learning Studio](use-sample-datasets.md)).
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This introductory topic also discusses how to get data ready for use in Machine Learning Studio and describes which data formats and data types are supported.
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This introductory article also discusses how to get data ready for use in Machine Learning Studio and describes which data formats and data types are supported.
Often enterprises that work with on-premises data would like to take advantage of the scale and agility of the cloud for their machine learning workloads. But they don't want to disrupt their current business processes and workflows by moving their on-premises data to the cloud. Azure Machine Learning now supports reading your data from an on-premises SQL Server database and then training and scoring a model with this data. You no longer have to manually copy and sync the data between the cloud and your on-premises server. Instead, the **Import Data** module in Azure Machine Learning Studio can now read directly from your on-premises SQL Server database for your training and scoring jobs.
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