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‎articles/machine-learning/concept-vulnerability-management.md

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By default, dependencies are layered on top of base images provided by Azure ML when building environments. You can also use your own base images when using environments in Azure Machine Learning. Once you install more dependencies on top of the Microsoft-provided images, or bring your own base images, vulnerability management becomes your responsibility.
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Associated to your Azure Machine Learning workspace is an Azure Container Registry instance that's used as a cache for container images. Any image materialized, is pushed to the container registry, and used if experimentation or deployment is triggered for the corresponding environment. Azure Machine Learning doesn't delete any image from your container registry, and it's your responsibility to evaluate the need of an image over time. To monitor and maintain environment hygiene, you can use [Microsoft Defender for Container Registry](/azure/defender-for-cloud/defender-for-container-registries-usage) to help scan your images for vulnerabilities. To automate your processes based on triggers from Microsoft Defender, see [Automate responses to Microsoft Defender for Cloud triggers](/azure/defender-for-cloud/workflow-automation).
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Associated to your Azure Machine Learning workspace is an Azure Container Registry instance that's used as a cache for container images. Any image materialized, is pushed to the container registry, and used if experimentation or deployment is triggered for the corresponding environment. Azure Machine Learning doesn't delete any image from your container registry, and it's your responsibility to evaluate the need of an image over time. To monitor and maintain environment hygiene, you can use [Microsoft Defender for Container Registry](../defender-for-cloud/defender-for-container-registries-usage.md) to help scan your images for vulnerabilities. To automate your processes based on triggers from Microsoft Defender, see [Automate responses to Microsoft Defender for Cloud triggers](../defender-for-cloud/workflow-automation.md).
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## Vulnerability management on compute hosts
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* Automated ML jobs run on environments that layer on top of Azure ML [base docker images](https://github.com/Azure/AzureML-Containers).
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* Designer jobs are compartmentalized into [Components](concept-designer.md#component). Each component has its own environment that layers on top of the Azure ML base docker images. For more information on components, see the [Component reference](/azure/machine-learning/component-reference/component-reference).
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* Designer jobs are compartmentalized into [Components](concept-designer.md#component). Each component has its own environment that layers on top of the Azure ML base docker images. For more information on components, see the [Component reference](./component-reference/component-reference.md).
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## Next steps
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* [Azure Machine Learning Base Images Repository](https://github.com/Azure/AzureML-Containers)
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* [Data Science Virtual Machine release notes](/azure/machine-learning/data-science-virtual-machine/release-notes)
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* [AzureML Python SDK Release Notes](/azure/machine-learning/azure-machine-learning-release-notes)
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* [Machine learning enterprise security](/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-enterprise-security)
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* [Data Science Virtual Machine release notes](./data-science-virtual-machine/release-notes.md)
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* [AzureML Python SDK Release Notes](./azure-machine-learning-release-notes.md)
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* [Machine learning enterprise security](/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-enterprise-security)

‎articles/machine-learning/data-science-virtual-machine/reference-known-issues.md

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### Virtual Machine Generation 2 (Gen 2) not working
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When you try to create Data Science VM based on Virtual Machine Generation 2 (Gen 2) it fails.
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Currently, we maintain and provide images for Data Science VM based on Windows 2019 Server only for Generation 1 virtual machines. [Gen 2](/azure/virtual-machines/generation-2) are not yet supported and we plan to support them in near future.
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Currently, we maintain and provide images for Data Science VM based on Windows 2019 Server only for Generation 1 virtual machines. [Gen 2](../../virtual-machines/generation-2.md) are not yet supported and we plan to support them in near future.
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### Accessing SQL Server
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![Enable Hyper-V](./media/workaround/hyperv-enable-dsvm.png)
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![Enable Hyper-V](./media/workaround/hyperv-enable-dsvm.png)

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