Difference between Pipelines in Azure Machine Learning services and Azure DevOps Pipelines?

Question

I've seen these tools but i don't know when exactly I have to choose one of them, which are the main differences?

Thank you, in advance.

Answer 1

Hello,

Could you please let us know the scenario that you would be using to help you choose the right service.

Pipelines in Azure Machine Learning service are basically used for data preparation such as normalizations and transformations, model training, model evaluation and deployment.

Azure pipelines are used for continuous integration and delivery of your application to any platform/any cloud. 

Here is a very simple canonical notation of these services that can help you differentiate when to use them.

Pipeline	What it does	Canonical pipe
Azure Machine Learning pipelines	Defines reusable machine learning workflows that can be used as a template for your machine learning scenarios.	Data -> model
Azure Data Factory pipelines	Groups data movement, transformation, and control activities needed to perform a task.	Data -> data
Azure pipelines	Continuous integration and delivery of your application to any platform/any cloud	Code -> app/service

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Answer 2

Azure Machine Learning pipelines :      Defines reusable machine learning workflows that can be used as a                                                                            template  for your machine learning scenarios.

Azure Data Factory pipelines:            Groups data movement, transformation, and control activities needed to                                                                   perform a task.

Azure Pipelines :                                Continuous integration and delivery of your application to any                                                                                   platform/any  cloud