In Azure machine Learning data stores or
abstractions for cloud data sources, They encapsulate the information
required to connect to data sources. You can access data stores
directly encode by using the Azure Machine Learning SDK and
use it to upload or download data. As your machine learning supports
the creation of data stores from multiple kinds of Azure data source, including
Azure storage blob and file containers. Azure data Lake stores as your SQL
database and Azure data bricks, file system or D B F S for a full list
of supported data stores, see the Azure machine Learning documentation from the
Microsoft website, you can find a link to this web page from the additional
readings at the end of this lesson. Every workspace has two
built in data stores and as your storage blob container and
an Azure storage file container that are used a system
storage by Azure Machine Learning there's also a 3rd data store that
gets added to your workspace. If you make use of the open datasets
provided as samples, for example, by creating a designer pipeline
based on a sample dataset. In most machine learning projects, you will likely need to work
with data sources of your own. This is because you will either need
to store larger volumes of data than the built in data store support or
because you need to integrate your machine learning solution with data
from existing applications