V2 and V1 comparison

Question

Hello, I am looking for some guidance about Azure machine learning SDK migration, I know that there’s a lot of difference between the two and I can’t find a place to help me out.

Can I get some help and suggestions?

Answer 1

Hello @otto atler

Thanks for reaching out to us, the document shared by Dillion is very helpful - https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?view=azureml-api-2

In the document page, left panel, you can see there is a list include different aspects - workspace, compute, datastore, data assets, model assets, every page it describes the difference of how to use the SDK1 and SDK2. I don't want to copy and paste the whole content, but you can get everything in the related topic page.

Like in the workspace page, it describes how to create workspace in both V1 and V2 -

I hope this helps, please let me know if you have any other questions.

Regards,

Yutong

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Answer 2

Hello Otto,

I'd recommend reading the following page:

Upgrade to v2

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2

You should use v2 if you're starting a new machine learning project or workflow. You should use v2 if you want to use the new features offered in v2. The features include:

• Managed Inferencing
• Reusable components in pipelines
• Improved scheduling of pipelines
• Responsible AI dashboard
• Registry of assets

Some feature gaps in v2 include:

• Spark support in jobs - this is currently in preview in v2.
• Publishing jobs (pipelines in v1) as endpoints. You can however, schedule pipelines without publishing.
• Support for SQL/database datastores.
• Ability to use classic prebuilt components in the designer with v2.

Hopefully this helps.

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