Examples are provided in the data.ipynb notebook that demonstrate how to use the AzureML SDK to:

1. Read/write data in a job.
2. Create a data asset to share with others in your team.
3. Create a data asset by importing data from external data sources.
4. Abstract schema for tabular data using MLTable.

Below are the links to the data documentation on docs.microsoft.com. The documentation includes code snippets.

• Data concepts in Azure Machine Learning: Learn about the 4 key data concepts in AzureML (URIs, Assets, Datastores, MLTable).
• Create a datastore: Learn how to create an AzureML datastore.
• Create a data asset: Learn how to create different data assets so that team members can easily discover common data.
• Import data from external sources and create a data asset: Learn how to create data assets by importing data from external data sources .
• Read/Write data in jobs: Learn how to read/write data in jobs - including URIs, MLTable and assets.
• Data administration guide: Documentation for Azure administrators to learn about the different permissions and authentication methods for data in AzureML.