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

  • 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.


    • Edited by VxAnalyst Thursday, June 20, 2019 7:39 PM
    Thursday, June 20, 2019 7:39 PM

Answers

  • 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
    -----------------------------------------------------------------------------------------------------------
    If you found this post helpful, please give it a "Helpful" vote. 
    Please remember to mark the replies as answers if they help. 





    Friday, June 21, 2019 2:03 PM

All replies

  • 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
    -----------------------------------------------------------------------------------------------------------
    If you found this post helpful, please give it a "Helpful" vote. 
    Please remember to mark the replies as answers if they help. 





    Friday, June 21, 2019 2:03 PM
  • 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

    Tuesday, November 5, 2019 5:57 AM