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Merge pull request #109511 from sdgilley/sdg-freshness-review
Sdg freshness review
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articles/machine-learning/concept-compute-target.md

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ms.topic: conceptual
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ms.author: sgilley
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ms.date: 11/04/2019
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ms.date: 03/30/2020
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# As a data scientist, I want to understand what a compute target is and why I need it.
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A managed compute resource is created and managed by Azure Machine Learning. This compute is optimized for machine learning workloads. Azure Machine Learning compute clusters and [compute instances](concept-compute-instance.md) are the only managed computes. Additional managed compute resources may be added in the future.
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You can create Azure Machine Learning compute instances (preview) or compute clusters in:
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You can create Azure Machine Learning compute instances (preview) or compute clusters from:
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* Azure Machine Learning studio
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* Azure portal
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* Python SDK [ComputeInstance](https://docs.microsoft.com/python/api/azureml-core/azureml.core.compute.computeinstance(class)?view=azure-ml-py) and [AmlCompute](https://docs.microsoft.com/python/api/azureml-core/azureml.core.compute.amlcompute(class)?view=azure-ml-py) classes
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* [R SDK](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-compute-targets)
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* Resource Manager template
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| | Azure Machine Learning studio | Azure portal | SDK | Resource Manager template | CLI |
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|---| ----- | ----- | ----- | ----- | ----- |
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| Compute instance | yes | yes | yes | yes | |
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| Compute cluster | yes | yes | yes | yes | yes |
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You can also create compute clusters using the [machine learning extension for the Azure CLI](tutorial-train-deploy-model-cli.md#create-the-compute-target-for-training).
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When created these compute resources are automatically part of your workspace unlike other kinds of compute targets.
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articles/machine-learning/how-to-tune-hyperparameters.md

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ms.date: 03/30/2020
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