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docs/ai-ml/architecture/batch-scoring-deep-learning-content.md

@@ -51,7 +51,7 @@ Processing involves the following steps:
 
 ### Potential use cases
 
-A media organization has a video whose style they want to change to look like a specific painting. The organization wants to apply this style to all frames of the video in a timely manner and in an automated fashion. For more background about neural style transfer algorithms, see [Image Style Transfer Using Convolutional Neural Networks][image-style-transfer] (PDF).
+A media organization has a video whose style they want to change to look like a specific painting. The organization wants to apply this style to all frames of the video in a timely manner and in an automated fashion. For more background about neural style transfer algorithms, see [Image Style Transfer Using Convolutional Neural Networks (PDF)][image-style-transfer].
 
 ## Considerations
 

docs/ai-ml/index.md

@@ -20,7 +20,7 @@ categories:
 
 # Artificial intelligence (AI) architecture design
 
-*Artificial intelligence* (AI) is the capability of a computer to imitate intelligent human behavior. Through AI, machines can analyze images, comprehend speech, interact in natural ways, and make predictions using data.
+*Artificial intelligence (AI)* is the capability of a computer to imitate intelligent human behavior. Through AI, machines can analyze images, comprehend speech, interact in natural ways, and make predictions using data.
 
 ![Illustration depicting the relationship of artificial intelligence as a parent concept. Within AI is machine learning. Within machine learning is deep learning.](_images/ai-overview-img-001.png)
 

docs/best-practices/data-partitioning-strategies-content.md

@@ -196,7 +196,7 @@ The ability to search for data is often the primary method of navigation and exp
 
 Azure Search stores searchable content as JSON documents in a database. You define indexes that specify the searchable fields in these documents and provide these definitions to Azure Search. When a user submits a search request, Azure Search uses the appropriate indexes to find matching items.
 
-To reduce contention, the storage that's used by Azure Search can be divided into 1, 2, 3, 4, 6, or 12 partitions, and each partition can be replicated up to 6 times. The product of the number of partitions multiplied by the number of replicas is called the *search unit* (SU). A single instance of Azure Search can contain a maximum of 36 SUs (a database with 12 partitions only supports a maximum of 3 replicas).
+To reduce contention, the storage that's used by Azure Search can be divided into 1, 2, 3, 4, 6, or 12 partitions, and each partition can be replicated up to 6 times. The product of the number of partitions multiplied by the number of replicas is called the *search unit (SU)*. A single instance of Azure Search can contain a maximum of 36 SUs (a database with 12 partitions only supports a maximum of 3 replicas).
 
 You are billed for each SU that is allocated to your service. As the volume of searchable content increases or the rate of search requests grows, you can add SUs to an existing instance of Azure Search to handle the extra load. Azure Search itself distributes the documents evenly across the partitions. No manual partitioning strategies are currently supported.
 

docs/databases/architecture/n-tier-cassandra-content.md

@@ -188,7 +188,7 @@ For more information, see the cost section in [Microsoft Azure Well-Architected
 
 ### Security
 
-Virtual networks are a traffic isolation boundary in Azure. VMs in one VNet can't communicate directly with VMs in a different VNet. VMs within the same VNet can communicate, unless you create [network security groups][nsg] (NSGs) to restrict traffic. For more information, see [Microsoft cloud services and network security][network-security].
+Virtual networks are a traffic isolation boundary in Azure. VMs in one VNet can't communicate directly with VMs in a different VNet. VMs within the same VNet can communicate, unless you create [network security groups (NSGs)][nsg] to restrict traffic. For more information, see [Microsoft cloud services and network security][network-security].
 
 For incoming Internet traffic, the load balancer rules define which traffic can reach the back end. However, load balancer rules don't support IP safe lists, so if you want to add certain public IP addresses to a safe list, add an NSG to the subnet.
 

docs/example-scenario/aks-agic/aks-agic-content.md

@@ -134,7 +134,7 @@ Although Kubernetes cannot guarantee perfectly secure isolation between tenants,
 
 *Download a [Visio file](https://arch-center.azureedge.net/aks-agic.vsdx) of this architecture.*
 
-The [Application Gateway Ingress Controller (AGIC)](/azure/application-gateway/ingress-controller-overview) is a Kubernetes application, which makes it possible for [Azure Kubernetes Service (AKS)](/azure/aks/intro-kubernetes) customers to use an [Azure Application Gateway](/azure/application-gateway/overview) to expose their containerized applications to the Internet. AGIC monitors the Kubernetes cluster that it is hosted on and continuously updates an Application Gateway, so that the selected services are exposed to the Internet. The Ingress Controller runs in its own pod on the customer's AKS instance. AGIC monitors a subset of Kubernetes Resources for changes. The state of the AKS cluster is translated to Application Gateway-specific configuration and applied to the [Azure Resource Manager (ARM)](/azure/azure-resource-manager/management/overview). This architecture sample shows proven practices to deploy a public or private [Azure Kubernetes Service (AKS) cluster](/azure/aks/intro-kubernetes), with an [Azure Application Gateway](/azure/application-gateway/overview) and an [Application Gateway Ingress Controller](/azure/application-gateway/ingress-controller-overview) add-on.
+The [Application Gateway Ingress Controller (AGIC)](/azure/application-gateway/ingress-controller-overview) is a Kubernetes application, which makes it possible for [Azure Kubernetes Service (AKS)](/azure/aks/intro-kubernetes) customers to use an [Azure Application Gateway](/azure/application-gateway/overview) to expose their containerized applications to the Internet. AGIC monitors the Kubernetes cluster that it is hosted on and continuously updates an Application Gateway, so that the selected services are exposed to the Internet. The Ingress Controller runs in its own pod on the customer's AKS instance. AGIC monitors a subset of Kubernetes Resources for changes. The state of the AKS cluster is translated to Application Gateway-specific configuration and applied to the [Azure Resource Manager](/azure/azure-resource-manager/management/overview). This architecture sample shows proven practices to deploy a public or private [Azure Kubernetes Service (AKS) cluster](/azure/aks/intro-kubernetes), with an [Azure Application Gateway](/azure/application-gateway/overview) and an [Application Gateway Ingress Controller](/azure/application-gateway/ingress-controller-overview) add-on.
 
 A single instance of the [Azure Application Gateway Kubernetes Ingress Controller (AGIC)](/azure/application-gateway/ingress-controller-multiple-namespace-support) can ingest events from and observe multiple namespaces. Should the AKS administrator decide to use Application Gateway as an ingress, all namespaces will use the same instance of Application Gateway. A single installation of Ingress Controller will monitor accessible namespaces and will configure the Application Gateway that it is associated with.
 

docs/example-scenario/azure-virtual-desktop/azure-virtual-desktop-content.md

@@ -152,7 +152,7 @@ Azure Virtual Desktop, much like Azure, has certain service limitations that you
 - We recommend that you deploy no more than 5,000 VMs per Azure subscription per region. This recommendation applies to both personal and pooled host pools, based on Windows Enterprise single and multi-session. Most customers use Windows Enterprise multi-session, which allows multiple users to log in to each VM. You can increase the resources of individual session-host VMs to accommodate more user sessions.
 - For automated session-host scaling tools, the limits are around 2,500 VMs per Azure subscription per region, because VM status interaction consumes more resources.
 - To manage enterprise environments with more than 5,000 VMs per Azure subscription in the same region, you can create multiple Azure subscriptions in a hub-spoke architecture and connect them via virtual network peering (using one subscription per spoke). You could also deploy VMs in a different region in the same subscription to increase the number of VMs.
-- Azure Resource Manager (ARM) subscription API throttling limits don't allow more than 600 Azure VM reboots per hour via the Azure portal. You can reboot all your machines at once via the operating system, which doesn't consume any Azure Resource Manager subscription API calls. For more information about counting and troubleshooting throttling limits based on your Azure subscription, see [Troubleshoot API throttling errors](/azure/virtual-machines/troubleshooting/troubleshooting-throttling-errors).
+- Azure Resource Manager subscription API throttling limits don't allow more than 600 Azure VM reboots per hour via the Azure portal. You can reboot all your machines at once via the operating system, which doesn't consume any Azure Resource Manager subscription API calls. For more information about counting and troubleshooting throttling limits based on your Azure subscription, see [Troubleshoot API throttling errors](/azure/virtual-machines/troubleshooting/troubleshooting-throttling-errors).
 - You can currently deploy up to 132 VMs in a single ARM template deployment in the Azure Virtual Desktop portal. To create more than 132 VMs, run the ARM template deployment in the Azure Virtual Desktop portal multiple times.
 - Azure VM session-host name prefixes can't exceed 11 characters, due to auto-assigning of instance names and the NetBIOS limit of 15 characters per computer account.
 - By default, you can deploy up to 800 instances of most resource types in a resource group. Azure Compute doesn't have this limit.

docs/example-scenario/forensics/index-content.md

@@ -83,7 +83,7 @@ Access to the target architecture includes the following roles:
 
 #### Azure Storage account
 
-The [Azure Storage account](/azure/storage/common/storage-account-overview) in the SOC subscription hosts the disk snapshots in a container configured with a *legal hold* policy as Azure immutable blob storage. Immutable blob storage stores business-critical data objects in a *write once, read many* (WORM) state, which makes the data nonerasable and uneditable for a user-specified interval.
+The [Azure Storage account](/azure/storage/common/storage-account-overview) in the SOC subscription hosts the disk snapshots in a container configured with a *legal hold* policy as Azure immutable blob storage. Immutable blob storage stores business-critical data objects in a *write once, read many (WORM)* state, which makes the data nonerasable and uneditable for a user-specified interval.
 
 Be sure to enable the [secure transfer](/azure/storage/common/storage-require-secure-transfer) and [storage firewall](/azure/storage/common/storage-network-security?tabs=azure-portal#grant-access-from-a-virtual-network) properties. The firewall grants access only from the SOC virtual network.
 

docs/example-scenario/infrastructure/devtest-labs-reference-architecture-content.md

@@ -101,7 +101,7 @@ The following links address governance and compliance for DTL:
 
 #### Identity and Access Management
 
-Enterprise organizations typically follow a least-privileged approach to operational access designed through Microsoft Entra ID, [Azure role-based access control](/azure/role-based-access-control/overview) (RBAC), and custom role definitions. The RBAC roles enable management of DTL resources, such as create virtual machines, create environments, and start, stop, restart, delete, and apply artifacts.
+Enterprise organizations typically follow a least-privileged approach to operational access designed through Microsoft Entra ID, [Azure role-based access control (RBAC)](/azure/role-based-access-control/overview), and custom role definitions. The RBAC roles enable management of DTL resources, such as create virtual machines, create environments, and start, stop, restart, delete, and apply artifacts.
 
 - Access to labs can be configured to segregate duties within your team into different [roles](/azure/devtest-labs/devtest-lab-add-devtest-user). Three of these RBAC roles are Owner, DevTest Labs User, and Contributor. The DTL resource should be owned by those who understand the project and team requirements for budget, machines, and required software. A common model is the project-lead or the app-admin as the lab owner and the team members as lab users. The Contributor role can be assigned to app-infra members who need permissions to manage lab resources. Lab owner is responsible for configuring the policies and adding the required users to the lab.
 - For enterprises that require users to connect with domain-based identities, a domain controller added to the Platform subscription can be used to domain-join DTL VMs. [DTL artifacts](/azure/devtest-labs/devtest-lab-concepts#artifacts) provide a way to domain-join VMs automatically. By default, DTL virtual machines use a local admin account.

docs/example-scenario/infrastructure/iaas-high-availability-disaster-recovery-content.md

@@ -12,7 +12,7 @@ This article presents a decision tree and examples of high-availability (HA) and
 
 The decision flowchart reflects the principle that HA apps should use availability zones if possible. Cross-zone, and therefore cross-datacenter, HA provides > 99.99% SLA because of resilience to datacenter failure.
 
-Availability sets and availability zones for different app tiers aren't guaranteed to be within the same datacenters. If app latency is a primary concern, you should colocate services in a single datacenter by using [proximity placement groups](https://azure.microsoft.com/blog/introducing-proximity-placement-groups) (PPGs) with availability zones and availability sets.
+Availability sets and availability zones for different app tiers aren't guaranteed to be within the same datacenters. If app latency is a primary concern, you should colocate services in a single datacenter by using [proximity placement groups (PPGs)](https://azure.microsoft.com/blog/introducing-proximity-placement-groups) with availability zones and availability sets.
 
 ### Components
 
@@ -74,7 +74,7 @@ Availability zones are suitable for many clustered app scenarios, including [Alw
 
 If you want to use a VM-based *cluster arbiter*, for example a *file-share witness*, place it in the third availability zone, to ensure quorum isn't lost if any one zone fails. Alternatively, you might be able to use a cloud-based witness in another region.
 
-All VMs in an availability zone are in a single *fault domain* (FD) and *update domain* (UD), meaning they share a common power source and network switch, and can all be rebooted at the same time. If you create VMs across different availability zones, your VMs are effectively distributed across different FDs and UDs, so they won't all fail or be rebooted at the same time. If you want to have redundant in-zone VMs as well as cross-zone VMs, you should place the in-zone VMs in availability sets in PPGs to ensure they won't all be rebooted at once. Even for single-instance VM workloads that aren't redundant today, you can still optionally use availability sets in the PPGs to allow for future growth and flexibility.
+All VMs in an availability zone are in a single *fault domain (FD)* and *update domain (UD)*, meaning they share a common power source and network switch, and can all be rebooted at the same time. If you create VMs across different availability zones, your VMs are effectively distributed across different FDs and UDs, so they won't all fail or be rebooted at the same time. If you want to have redundant in-zone VMs as well as cross-zone VMs, you should place the in-zone VMs in availability sets in PPGs to ensure they won't all be rebooted at once. Even for single-instance VM workloads that aren't redundant today, you can still optionally use availability sets in the PPGs to allow for future growth and flexibility.
 
 For deploying virtual machine scale sets across availability zones, consider using [Orchestration mode](/azure/virtual-machine-scale-sets/orchestration-modes-api-comparison), currently in public preview, which allows combining FDs and availability zones.
 

docs/guide/hadoop/apache-hbase-migration-content.md

@@ -61,7 +61,7 @@ The following diagram illustrates these concepts.
 HBase uses a combination of data structures that reside in memory and in persistent storage to deliver fast writes. When a write occurs, the data is first written to a write-ahead log (WAL), which is a data structure that's stored on persistent storage. The role of the WAL is to track changes so that logs can be replayed in case of a server failure. The WAL is only used for resiliency.
 After data is committed to the WAL, it's written to MemStore, which is an in-memory data structure. At this stage, a write is complete.
 
-For long-term data persistence, HBase uses a data structure called an *HBase file* (HFile). An HFile is stored on HDFS. Depending on MemStore size and the data flush interval, data from MemStore is written to an HFile. For information about the format of an HFile, see [Appendix G: HFile format](https://HBase.apache.org/book.html#_hfile_format_2).
+For long-term data persistence, HBase uses a data structure called an *HBase file (HFile)*. An HFile is stored on HDFS. Depending on MemStore size and the data flush interval, data from MemStore is written to an HFile. For information about the format of an HFile, see [Appendix G: HFile format](https://HBase.apache.org/book.html#_hfile_format_2).
 
 The following diagram shows the steps of a write operation.
 

docs/guide/hadoop/apache-sqoop-migration-content.md

@@ -236,7 +236,7 @@ For more information, see [What is a private endpoint?](/azure/private-link/priv
 
 Sqoop improves data transfer performance by using MapReduce for parallel processing. After you migrate Sqoop, Data Factory can adjust performance and scalability for scenarios that perform large-scale data migrations.
 
- A *data integration unit* (DIU) is a Data Factory unit of performance. It's a combination of CPU, memory, and network resource allocation. Data Factory can adjust up to 256 DIUs for copy activities that use the Azure integration runtime. For more information, see [Data Integration Units](/azure/data-factory/copy-activity-performance#data-integration-units).
+ A *data integration unit (DIU)* is a Data Factory unit of performance. It's a combination of CPU, memory, and network resource allocation. Data Factory can adjust up to 256 DIUs for copy activities that use the Azure integration runtime. For more information, see [Data Integration Units](/azure/data-factory/copy-activity-performance#data-integration-units).
 
 If you use self-hosted integration runtime, you can improve performance by scaling the machine that hosts the self-hosted integration runtime. The maximum scale-out is four nodes.
 

docs/guide/iot-edge-vision/index.md

@@ -22,7 +22,7 @@ ms.custom:
 
 This series of articles describes how to plan and design a computer vision workload that uses [Azure IoT Edge](https://azure.microsoft.com/services/iot-edge). You can run Azure IoT Edge on devices, and integrate with Azure Machine Learning, Azure Storage, Azure App Services, and Power BI for end-to-end vision AI solutions.
 
-Visually inspecting products, resources, and environments is critical for many endeavors. Human visual inspection and analytics are subject to inefficiency and inaccuracy. Enterprises now use deep learning artificial neural networks called *convolutional neural networks* (CNNs) to emulate human vision. Using CNNs for automated image input and analysis is commonly called *computer vision* or *vision AI*.
+Visually inspecting products, resources, and environments is critical for many endeavors. Human visual inspection and analytics are subject to inefficiency and inaccuracy. Enterprises now use deep learning artificial neural networks called *convolutional neural networks (CNNs)* to emulate human vision. Using CNNs for automated image input and analysis is commonly called *computer vision* or *vision AI*.
 
 Technologies like containerization support portability, which allows migrating vision AI models to the network edge. You can train vision inference models in the cloud, containerize the models, and use them to create custom modules for Azure IoT Edge runtime-enabled devices. Deploying vision AI solutions at the edge yields performance and cost benefits.