Notes about Azure ML, Part 1 - Datasets and Datastores

December 23, 2021
machine-learning azure ml dataset datastore

In AzureML, the two essential concepts that help us work with data are Datastores and Datasets.

Datastores and Datasets

Datastores

Azure has various locations for storing data, such as;

These are the places where the data can exist.

An Azure storage account is a container for all the Azure Storage data objects blobs, file shares, queues, tables, and disks, making them accessible from anywhere in the world over HTTP or HTTPS.

When we create an AzureML resource, an associated storage account is also created. This account will contain two built-in datastores;

These will contain the relevant data and code for the AzureML resource.

Creating a Datastore

Datasets

Datasets are those assets in the Machine learning Workspace where we connect to the data in storage services so that the data is made available for ML Experiments. Hence when creating a dataset, we create a reference to the data in storage services. Information is not copied from the storage service to the workspace for several reasons.

Datasets are created in a number of ways.

Creating a Dataset

Datasets can be

Viewing a Dataset




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