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Copy file name to clipboardExpand all lines: articles/machine-learning/how-to-retrain-designer.md
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@@ -58,6 +58,8 @@ Create pipeline parameters to dynamically set variables at runtime. For this exa
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1. Mouseover the **Path** field, and select the ellipses above the **Path** field that appear.
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1. Select **Add to pipeline parameter**.
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1. Provide a parameter name and a default value.
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1. Select **Save**.
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1. Submit the pipeline run.
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## Find a trained model
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1. Wait for the pipeline to finish running.
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1. Select the **Train Model** module.
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1. In the module details pane, to the right of the canvas, select **Outputs + logs**.
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1. You can find mind your model in **Other outputs** along with the run logs.
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1. You can find your model in **Other outputs** along with run logs.
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1. Alternatively, select the **View output** icon. From here, you can follow the instruction in the dialog to navigate directly to your datastore.
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## Publish a training pipeline
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You can publish a pipeline to a pipeline endpoint to easily reuse your pipelines in the future. A pipeline endpoint creates a REST endpoint to call your pipeline for future runs. In this example, your pipeline endpoint lets you reuse your pipeline to retrain a model on different data.
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Publish a pipeline to a pipeline endpoint to easily reuse your pipelines in the future. A pipeline endpoint creates a REST endpoint to invoke pipeline in the future. In this example, your pipeline endpoint lets you reuse your pipeline to retrain a model on different data.
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1. Select **Publish** above the designer canvas.
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1. Select or create a pipeline endpoint.
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> [!NOTE]
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> You can publish multiple pipelines to a single endpoint. Each pipeline in the endpoint is given a version number, which you can specify when you call the pipeline endpoint.
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> You can publish multiple pipelines to a single endpoint. Each pipeline in a given endpoint is given a version number, which you can specify when you call the pipeline endpoint.
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