Hello @V JEEVA
Thanks for using Microsoft Q&A platform. Let me answer your questions one by one.
where can I learn more about azure machine learning
The best way to learn Azure Machine Learning is the documentation, please refer to - https://azure.microsoft.com/en-us/services/machine-learning/#documentation
How to mount the dataset in ML Studio using python sdk ?
Generally there are two ways to work with data in Azure Machine Learning -
Use datastores - https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?tabs=cli-identity-based-access%2Ccli-adls-identity-based-access%2Ccli-azfiles-account-key%2Ccli-adlsgen1-identity-based-access
Use data assets - https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?tabs=cli-identity-based-access%2Ccli-adls-identity-based-access%2Ccli-azfiles-account-key%2Ccli-adlsgen1-identity-based-access
What are the different ways and which is the right one ?
It depends on your need. Compared to data assets and datastore, the benefits of creating data assets are:
You can share and reuse data with other members of the team such that they do not need to remember file locations.
You can seamlessly access data during model training (on any supported compute type) without worrying about connection strings or data paths.
You can version the data.
Can we create dataset pointing to two different stores ?
If you need to assembly data, you may want to consider data assets. By creating a data asset, you create a reference to the data source location, along with a copy of its metadata. Because the data remains in its existing location, you incur no extra storage cost, and don't risk the integrity of your data sources. You can create Data from datastores, Azure Storage, public URLs, and local files.
I hope this helps!
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