Merge pull request #227806 from mrbullwinkle/mrb_02_17_2023_openai_branding

[Cognitive Services] [Azure OpenAI] Bulk Update

Author: v-dirichards

articles/cognitive-services/openai/concepts/content-filter.md

@@ -1,7 +1,7 @@
 ---
-title: Azure OpenAI content filtering
+title: Azure OpenAI Service content filtering
 titleSuffix: Azure OpenAI
-description: Learn about the content filtering capabilities of the OpenAI service in Azure Cognitive Services
+description: Learn about the content filtering capabilities of Azure OpenAI in Azure Cognitive Services
 author: chrishMSFT
 ms.author: chrhoder
 ms.service: cognitive-services

articles/cognitive-services/openai/concepts/models.md

@@ -1,5 +1,5 @@
 ---
-title: Azure OpenAI models
+title: Azure OpenAI Service models
 titleSuffix: Azure OpenAI
 description: Learn about the different models that are available in Azure OpenAI. 
 ms.service: cognitive-services
@@ -13,7 +13,7 @@ recommendations: false
 keywords: 
 ---
 
-# Azure OpenAI models
+# Azure OpenAI Service models
 
 The service provides access to many different models, grouped by family and capability. A model family typically associates models by their intended task. The following table describes model families currently available in Azure OpenAI. Not all models are available in all regions currently. Please refer to the capability table at the bottom for a full breakdown. 
 

articles/cognitive-services/openai/concepts/understand-embeddings.md

@@ -1,5 +1,5 @@
 ---
-title: Azure OpenAI embeddings
+title: Azure OpenAI Service embeddings
 titleSuffix: Azure OpenAI - embeddings and cosine similarity
 description: Learn more about Azure OpenAI embeddings API for document search and cosine similarity
 services: cognitive-services
@@ -14,7 +14,7 @@ recommendations: false
 ms.custom:
 ---
 
-# Understanding embeddings in Azure OpenAI
+# Understanding embeddings in Azure OpenAI Service
 
 An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar.
 

articles/cognitive-services/openai/encrypt-data-at-rest.md

@@ -1,5 +1,5 @@
 ---
-title: Azure OpenAI encryption of data at rest
+title: Azure OpenAI Service encryption of data at rest
 description: Learn how Azure OpenAI encrypts your data when it's persisted to the cloud.
 titleSuffix: Azure Cognitive Services
 author: mrbullwinkle
@@ -10,7 +10,7 @@ ms.date: 11/14/2022
 ms.author: mbullwin
 ---
 
-# Azure OpenAI encryption of data at rest
+# Azure OpenAI Service encryption of data at rest
 
 Azure OpenAI automatically encrypts your data when it's persisted to the cloud. The encryption protects your data and helps you meet your organizational security and compliance commitments. This article covers how Azure OpenAI handles encryption of data at rest, specifically training data and fine-tuned models. For information on how data provided by you to the service is processed, used, and stored, consult the [data, privacy, and security article](/legal/cognitive-services/openai/data-privacy?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext).
 

articles/cognitive-services/openai/how-to/business-continuity-disaster-recovery.md

@@ -1,5 +1,5 @@
 ---
-title: 'Business Continuity and Disaster Recovery (BCDR) with Azure OpenAI'
+title: 'Business Continuity and Disaster Recovery (BCDR) with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
 description: Considerations for implementing Business Continuity and Disaster Recovery (BCDR) with Azure OpenAI 
 services: cognitive-services
@@ -15,9 +15,9 @@ keywords:
 
 ---
 
-# Business Continuity and Disaster Recovery (BCDR) considerations with Azure OpenAI
+# Business Continuity and Disaster Recovery (BCDR) considerations with Azure OpenAI Service
 
-The Azure OpenAI service is available in two regions. Since subscription keys are region bound, when a customer acquires a key, they select the region in which their deployments will reside and from then on, all operations stay associated with that Azure server region.  
+Azure OpenAI is available in two regions. Since subscription keys are region bound, when a customer acquires a key, they select the region in which their deployments will reside and from then on, all operations stay associated with that Azure server region.  
 
 It's rare, but not impossible, to encounter a network issue that hits an entire region. If your service needs to always be available, then you should design it to either fail-over into another region or split the workload between two or more regions. Both approaches require at least two OpenAI resources in different regions. This article provides general recommendations for how to implement  Business Continuity and Disaster Recovery (BCDR) for your Azure OpenAI applications.
 

articles/cognitive-services/openai/how-to/completions.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to generate text with Azure OpenAI'
+title: 'How to generate text with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
 description: Learn how to generate or manipulate text, including code with Azure OpenAI
 services: cognitive-services
@@ -19,7 +19,7 @@ keywords:
 
 The completions endpoint can be used for a wide variety of tasks. It provides a simple but powerful text-in, text-out interface to any of our [models](../concepts/models.md). You input some text as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. For example, if you give the API the prompt, "As Descartes said, I think, therefore", it will return the completion " I am" with high probability.
 
-The best way to start exploring completions is through our playground in the [Azure OpenAI Studio](https://oai.azure.com). It's a simple text box where you can submit a prompt to generate a completion. You can start with a simple example like the following:
+The best way to start exploring completions is through our playground in [Azure OpenAI Studio](https://oai.azure.com). It's a simple text box where you can submit a prompt to generate a completion. You can start with a simple example like the following:
 
 `write a tagline for an ice cream shop`
 

articles/cognitive-services/openai/how-to/create-resource.md

@@ -1,5 +1,5 @@
 ---
-title: 'How-to - Create a resource and deploy a model using Azure OpenAI'
+title: 'How-to - Create a resource and deploy a model using Azure OpenAI Service'
 titleSuffix: Azure OpenAI
 description: Walkthrough on how to get started with Azure OpenAI and make your first resource and deploy your first model.
 services: cognitive-services

articles/cognitive-services/openai/how-to/embeddings.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to generate embeddings with Azure OpenAI'
+title: 'How to generate embeddings with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
 description: Learn how to generate embeddings with Azure OpenAI
 services: cognitive-services

articles/cognitive-services/openai/how-to/fine-tuning.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to customize a model with Azure OpenAI'
+title: 'How to customize a model with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
 description: Learn how to create your own customized model with Azure OpenAI
 services: cognitive-services
@@ -16,7 +16,7 @@ keywords:
 ---
 # Learn how to customize a model for your application
 
-The Azure OpenAI Service lets you tailor our models to your personal datasets using a process known as *fine-tuning*. This customization step will let you get more out of the service by providing:
+Azure OpenAI Service lets you tailor our models to your personal datasets using a process known as *fine-tuning*. This customization step will let you get more out of the service by providing:
 
 - Higher quality results than what you can get just from prompt design
 - The ability to train on more examples than can fit into a prompt

articles/cognitive-services/openai/how-to/integrate-synapseml.md

@@ -1,5 +1,5 @@
 ---
-title: 'How-to - Use Azure OpenAI with large datasets'
+title: 'How-to - Use Azure OpenAI Service with large datasets'
 titleSuffix: Azure OpenAI
 description: Walkthrough on how to integrate Azure OpenAI with SynapseML and Apache Spark to apply large language models at a distributed scale.
 services: cognitive-services
@@ -15,14 +15,14 @@ recommendations: false
 
 # Use Azure OpenAI with large datasets
 
-The Azure OpenAI service can be used to solve a large number of natural language tasks through prompting the completion API. To make it easier to scale your prompting workflows from a few examples to large datasets of examples, we have integrated the Azure OpenAI service with the distributed machine learning library [SynapseML](https://www.microsoft.com/research/blog/synapseml-a-simple-multilingual-and-massively-parallel-machine-learning-library/). This integration makes it easy to use the [Apache Spark](https://spark.apache.org/) distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models at a distributed scale using Azure Open AI and Azure Synapse Analytics.
+Azure OpenAI can be used to solve a large number of natural language tasks through prompting the completion API. To make it easier to scale your prompting workflows from a few examples to large datasets of examples, we have integrated the Azure OpenAI service with the distributed machine learning library [SynapseML](https://www.microsoft.com/research/blog/synapseml-a-simple-multilingual-and-massively-parallel-machine-learning-library/). This integration makes it easy to use the [Apache Spark](https://spark.apache.org/) distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models at a distributed scale using Azure Open AI and Azure Synapse Analytics.
 
 ## Prerequisites
 
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
-- Access granted to the Azure OpenAI service in the desired Azure subscription
+- Access granted to Azure OpenAI in the desired Azure subscription
 
-    Currently, access to this service is granted only by application. You can apply for access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
+    Currently, access to this service is granted only by application. You can apply for access to Azure OpenAI by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - An Azure OpenAI resource – [create a resource](create-resource.md?pivots=web-portal#create-a-resource)
 - An Apache Spark cluster with SynapseML installed - create a serverless Apache Spark pool [here](../../../synapse-analytics/get-started-analyze-spark.md#create-a-serverless-apache-spark-pool)
 
@@ -39,15 +39,15 @@ The next step is to add this code into your Spark cluster. You can either create
 
 ## Fill in your service information
 
-Next, edit the cell in the notebook to point to your service. In particular, set the `resource_name`, `deployment_name`, `location`, and `key` variables to the corresponding values for your Azure OpenAI service.
+Next, edit the cell in the notebook to point to your service. In particular, set the `resource_name`, `deployment_name`, `location`, and `key` variables to the corresponding values for your Azure OpenAI resource.
 
 > [!IMPORTANT]
 > Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like [Azure Key Vault](../../../key-vault/general/overview.md). See the Cognitive Services [security](../../cognitive-services-security.md) article for more information.
 
 ```python
 import os
 
-# Replace the following values with your Azure OpenAI service information
+# Replace the following values with your Azure OpenAI resource information
 resource_name = "RESOURCE_NAME"      # The name of your Azure OpenAI resource.
 deployment_name = "DEPLOYMENT_NAME"  # The name of your Azure OpenAI deployment.
 location = "RESOURCE_LOCATION"       # The location or region ID for your resource.
@@ -175,7 +175,7 @@ display(completed_autobatch_df)
 
 ### Prompt engineering for translation
 
-The Azure OpenAI service can solve many different natural language tasks through [prompt engineering](completions.md). Here, we show an example of prompting for language translation:
+Azure OpenAI can solve many different natural language tasks through [prompt engineering](completions.md). Here, we show an example of prompting for language translation:
 
 ```python
 translate_df = spark.createDataFrame(