Classic pipeline

Jason Heen 40 Reputation points
2023-09-30T20:03:05.63+00:00

Hi, I want to learn the difference between classic pipeline and custom pipeline, when the elements are so different in the two ways? How can I use the elements in classic pipeline in my custom one?

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
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  1. YutongTie-MSFT 46,996 Reputation points
    2023-10-01T18:35:45.4266667+00:00

    Hello @Jason Heen

    Thanks for reaching out to us, to answer your question quick, no you can not mix the two elements.

    Designer supports two types of components, classic prebuilt components (v1) and custom components(v2). These two types of components are NOT compatible.

    Classic prebuilt components support typical data processing and machine learning tasks including regression and classification. Though classic prebuilt components will continue to be supported, no new components will be added.

    Custom components allow you to wrap your own code as a component enabling sharing across workspaces and seamless authoring across the Azure Machine Learning Studio, CLI v2, and SDK v2 interfaces.

    For new projects, we highly recommend that you use custom components since they are compatible with AzureML V2 and will continue to receive new updates.

    This article applies to custom components. - https://learn.microsoft.com/en-us/azure/machine-learning/concept-designer?view=azureml-api-2

    Regards,

    Yutong

    I hope this helps, please kindly accept the answer and vote 'Yes' if you feel helpful to support the community, thanks a lot.

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  1. S.Sengupta 15,601 Reputation points MVP
    2023-10-01T01:41:39.3633333+00:00

    A "classic pipeline" in data engineering and ETL refers to a fixed, sequential process for data processing, whereas a "custom pipeline" in machine learning refers to a flexible and customized workflow for developing and deploying machine learning models.

    The key difference is in their domains and purposes, with classic pipelines being focused on data processing and custom pipelines being focused on machine learning model development.

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