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articles/api-management/how-to-server-sent-events.md

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Follow these guidelines when using API Management to reach a backend API that implements SSE.
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* **Choose service tier for long-running HTTP connections** - SSE relies on a long-running HTTP connection that is supported in certain API Management [service tiers](api-management-key-concepts.md#api-management-tiers). Long-running connections are supported in the Classic and V2 API Management tiers, but not in the Consumption tier.
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* **Choose service tier for long-running HTTP connections** - SSE relies on a long-running HTTP connection that is supported in certain API Management [pricing tiers](api-management-key-concepts.md#api-management-tiers). Long-running connections are supported in the classic and v2 API Management tiers, but not in the Consumption tier.
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* **Keep idle connections alive** - If a connection between client and backend could be idle for 4 minutes or longer, implement a mechanism to keep the connection alive. For example, enable a TCP keepalive signal at the backend of the connection, or send traffic from the client side at least once per 4 minutes.
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articles/machine-learning/how-to-create-component-pipeline-python.md

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- sdkv2
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- build-2023
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- ignite-2023
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- update-code
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---
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# Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2
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To define the input data of a job that references the Web-based data, run:
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```
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[!notebook-python[] (~/azureml-examples-main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/image_classification_keras_minist_convnet.ipynb?name=define-input)]
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```
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[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/image_classification_keras_minist_convnet.ipynb?name=define-input)]
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By defining an `Input`, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred.
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