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You can replace auth_mode with key for key auth, or aml_token for azure machine learning token auth In this article, you'll learn how to programmatically schedule a pipeline to run on azure In this example, you use aad_token for microsoft entra token auth.

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To build your pipeline, you must create and build containers using docker images You may need to configure virtual network peering or vpn connections to enable access. Containers are used to abstract the dependencies for each step of the pipeline

You can manage your containers using azure's portal, specifically using the container registry to store the containers in the cloud.

Learn how to set up authentication to your azure machine learning workspace Authentication to your azure machine learning workspace is based on azure active directory (azure ad) for most things In general, there are four authentication workflows that you can use when connecting to the workspace: In this article, you learn how to manage access (authorization) to an azure machine learning workspace

You can create a pipeline endpoint that has multiple published pipelines behind it This technique gives you a fixed rest endpoint as you iterate on and update your machine learning pipelines. The azure ml pipeline helper class amlpipelinehelper in the shrike package was developed with the goal of helping data scientists to more easily create reusable pipelines. You must have an access policy in azure key vault that grants get, wrap, and unwrap access to the azure cosmos db application

Check firewall and network settings

If the data assets are hosted in a private network, make sure that the firewall and network settings allow the pipeline to access the data