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azure-resources/ApiManagement/service/kql/af4f88cb-35e8-4371-b29e-3a32b1d2f40a.kql
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// cannot-be-validated-with-arg |
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azure-resources/ApiManagement/service/kql/badd9a1f-222a-498d-ab84-1f14892b1c1b.kql
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// under-development |
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azure-resources/CognitiveServices/accounts/kql/081fc8a4-b2d9-405b-b351-334e621016f5.kql
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azure-resources/CognitiveServices/accounts/kql/61187af4-7d36-4b48-b16e-de78bef143a0.kql
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// cannot-be-validated-with-arg |
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--- | ||
title: MachineLearningServices | ||
geekdocCollapseSection: true | ||
geekdocHidden: true | ||
geekdocHidden: false | ||
--- |
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--- | ||
title: registries | ||
geekdocCollapseSection: true | ||
geekdocHidden: false | ||
--- | ||
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{{< azure-resources-recommendationlist name="azure-resources-recommendationlist" >}} |
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...resources/MachineLearningServices/registries/kql/611c7c96-ac55-4ddf-909e-9eb70fd048b7.kql
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// cannot-be-validated-with-arg |
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azure-resources/MachineLearningServices/registries/recommendations.yaml
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- description: Create the Azure machine learning registry in multiple regions | ||
aprlGuid: 611c7c96-ac55-4ddf-909e-9eb70fd048b7 | ||
recommendationTypeId: null | ||
recommendationControl: DisasterRecovery | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/registries | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
Azure Machine Learning registries enables to create and use assets (models, env, components, and datasets) in different workspaces. Registries support multi-region replication for low latency access to assets, allowing to use them in workspaces located in different Azure regions. | ||
potentialBenefits: Improves performance and reliability | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Manage Azure Machine Learning registries | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2&tabs=cli" |
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--- | ||
title: workspaces | ||
geekdocCollapseSection: true | ||
geekdocHidden: true | ||
geekdocHidden: false | ||
--- | ||
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{{< azure-resources-recommendationlist name="azure-resources-recommendationlist" >}} |
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azure-resources/MachineLearningServices/workspaces/kql/13794a63-8d95-47ce-acbd-5925ede5b208
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azure-resources/MachineLearningServices/workspaces/kql/48ea6480-6263-40ba-8937-326d790e63f6
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azure-resources/MachineLearningServices/workspaces/kql/675d249a-9486-45e3-8e89-863f5802782d
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azure-resources/MachineLearningServices/workspaces/kql/6e2af91f-477d-46a5-b8ce-6cd1b8176550
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azure-resources/MachineLearningServices/workspaces/kql/6e4f0fd1-1853-4b94-9736-6d6d239d2694
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azure-resources/MachineLearningServices/workspaces/kql/a86ed26a-59d9-47bd-b440-6bc71b843978
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azure-resources/MachineLearningServices/workspaces/kql/cf2569bb-1cf2-46ce-8885-d742dc6f4a4c
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// cannot-be-validated-with-arg |
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azure-resources/MachineLearningServices/workspaces/recommendations.yaml
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- description: Plan for a multi-regional deployment of Azure Machine Learning and associated resources | ||
aprlGuid: a86ed26a-59d9-47bd-b440-6bc71b843978 | ||
recommendationTypeId: null | ||
recommendationControl: DisasterRecovery | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
Ensure you have a recovery strategy defined. Check regional availability and paired regions. Machine Learning doesn't have auto failover. Therefore, you must design a strategy that encompasses the workspace and all its dependencies, such as Key Vault, Azure Storage, and Container Registry. | ||
potentialBenefits: Provides multi-region disaster recovery strategy | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Plan for multi-regional deployment | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2" | ||
|
||
- description: Deploy Azure Machine learning workspace in secondary region | ||
aprlGuid: 675d249a-9486-45e3-8e89-863f5802782d | ||
recommendationTypeId: null | ||
recommendationControl: DisasterRecovery | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
If your primary workspace is unavailable, switch to the secondary workspace to continue work. Azure Machine Learning doesn't auto-submit jobs to the secondary workspace during an outage. Update your code configuration to point to the new workspace resource. | ||
potentialBenefits: Provides recovery from regional outages | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Failover for business continuity and disaster recovery | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2" | ||
|
||
- description: Ensure to create Machine Learning Compute resources in secondary region | ||
aprlGuid: 13794a63-8d95-47ce-acbd-5925ede5b208 | ||
recommendationTypeId: null | ||
recommendationControl: DisasterRecovery | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
Create compute resources for training a Machine Learning model in selected regions. Ensure both regions have sufficient compute quota for dynamic scaling. Customers must configure HA across zones for attached compute resources like AKS, Azure Databricks, Container Instances. | ||
potentialBenefits: High availability and disaster recovery | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Failover for business continuity and disaster recovery | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2" | ||
- name: Compute targets in Azure Machine Learning | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target?view=azureml-api-2" | ||
|
||
- description: Selecting regions for BCDR, ensure that both regions offer adequate compute quotas | ||
aprlGuid: 6e4f0fd1-1853-4b94-9736-6d6d239d2694 | ||
recommendationTypeId: null | ||
recommendationControl: DisasterRecovery | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
When selecting regions for BCDR, ensure that both regions offer adequate compute quotas to meet your requirements in the same SKU. This ensures that you can failover to the secondary region without any issues. | ||
potentialBenefits: Provide enough compute resources to the secondary region | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Manage resource quotas | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2" | ||
|
||
- description: Choose SKUs with longer terms and avoid those nearing retirement | ||
aprlGuid: 6e2af91f-477d-46a5-b8ce-6cd1b8176550 | ||
recommendationTypeId: null | ||
recommendationControl: ServiceUpgradeAndRetirement | ||
recommendationImpact: Medium | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
When choosing SKUs, opt for those that support longer terms and steer clear of any SKUs that are nearing retirement. This ensures that you can continue to use the SKU for a longer period of time. | ||
potentialBenefits: supportability, longer term support | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: What are compute targets in Azure Machine Learning | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target?view=azureml-api-2#supported-vm-series-and-sizes" | ||
|
||
- description: Avoid NC and NC_Promo series Azure VMs for machine learning quotas; migrate to newer versions | ||
aprlGuid: cf2569bb-1cf2-46ce-8885-d742dc6f4a4c | ||
recommendationTypeId: null | ||
recommendationControl: ServiceUpgradeAndRetirement | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
Avoid selecting NC and NC_Promo series Azure virtual machines for machine learning VM quotas and make sure to migrate to newer versions. | ||
potentialBenefits: Avoid service disruption, longer term support | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: NC sizes series | ||
url: "https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/gpu-accelerated/nc-series?tabs=sizebasic" | ||
- name: Migration Guide for GPU Compute Workloads in Azure | ||
url: "https://learn.microsoft.com/en-us/azure/virtual-machines/n-series-migration" | ||
|
||
- description: Make Azure Machine Learning quota requests through the Azure Machine Learning Studio | ||
aprlGuid: 48ea6480-6263-40ba-8937-326d790e63f6 | ||
recommendationTypeId: null | ||
recommendationControl: OtherBestPractices | ||
recommendationImpact: High | ||
recommendationResourceType: Microsoft.MachineLearningServices/workspaces | ||
recommendationMetadataState: Active | ||
longDescription: | | ||
Requests for additional Azure Machine Learning quota should be made through the Azure Machine Learning Studio instead of the subscription level in the Azure portal. | ||
potentialBenefits: Scalability,capacity planning | ||
pgVerified: false | ||
automationAvailable: false | ||
tags: null | ||
learnMoreLink: | ||
- name: Manage and increase quotas and limits for resources with Azure Machine Learning | ||
url: "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2" |
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