Published_pipelines = PublishedPipeline.list(ws)įor published_pipeline in published_pipelines: You can get these values with the following code: import reįrom import Pipeline, PublishedPipelineįrom import Experiment To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule. Trigger pipelines with Azure Machine Learning SDK for Python You can use the one built in Create and run machine learning pipelines with Azure Machine Learning SDK. For more information, see Create and manage reusable environments for training and deployment with Azure Machine Learning.Ī Machine Learning workspace with a published pipeline. If you don’t have an Azure subscription, create a free account.Ī Python environment in which the Azure Machine Learning SDK for Python is installed. Azure Data Factory pipelines allow you to call a machine learning pipeline as part of a larger data orchestration pipeline. An Azure Logic App allows for more complex triggering logic or behavior.
Finally, you'll learn how to use other Azure services, Azure Logic App and Azure Data Factory, to run pipelines. After learning how to create schedules, you'll learn how to retrieve and deactivate them. Change-based schedules can be used to react to irregular or unpredictable changes, such as new data being uploaded or old data being edited.
Time-based schedules can be used to take care of routine tasks, such as monitoring for data drift. You can create a schedule based on elapsed time or on file-system changes. In this article, you'll learn how to programmatically schedule a pipeline to run on Azure.