Try the free or paid version of Azure Machine Learning. If you don't have an Azure subscription, create a free account before you begin. Pipelines can read and write data to and from supported Azure Storage locations. ML pipelines execute on compute targets (see What are compute targets in Azure Machine Learning). The ML pipelines you create are visible to the members of your Azure Machine Learning workspace. ![]() While you can use a different kind of pipeline called an Azure Pipeline for CI/CD automation of ML tasks, that type of pipeline isn't stored in your workspace. For guidance on creating your first pipeline, see Tutorial: Build an Azure Machine Learning pipeline for batch scoring or Use automated ML in an Azure Machine Learning pipeline in Python. ML pipelines are ideal for batch scoring scenarios, using various computes, reusing steps instead of rerunning them, and sharing ML workflows with others. Track ML pipelines to see how your model is performing in the real world and to detect data drift. ![]() Then, publish that pipeline for later access or sharing with others. Use ML pipelines to create a workflow that stitches together various ML phases. In this article, you learn how to create and run machine learning pipelines by using the Azure Machine Learning SDK.
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