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‎articles/ai-services/content-safety/overview.md

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| Australia East | ✅ | | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Canada East | | | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Central US | | | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| East US | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| East US | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| East US 2 | | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| France Central | | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Japan East | | | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |

‎articles/ai-services/openai/how-to/use-your-data-securely.md

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| `Storage Blob Data Contributor` | Azure OpenAI | Storage Account | Reads from the input container, and writes the preprocessed result to the output container. |
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| `Cognitive Services OpenAI Contributor` | Azure AI Search | Azure OpenAI | Custom skill. |
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| `Storage Blob Data Reader` | Azure AI Search | Storage Account | Reads document blobs and chunk blobs. |
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| `Reader` | Azure AI Studio Project | Azure Storage Private Endpoints (Blob & File) | Read search indexes created in blob storage within an AI Studio Project. |
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| `Cognitive Services OpenAI User` | Web app | Azure OpenAI | Inference. |
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‎articles/ai-services/openai/includes/model-matrix/global-batch.md

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manager: nitinme
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ms.service: azure-ai-openai
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ms.topic: include
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ms.date: 10/31/2024
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ms.date: 11/07/2024
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---
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| **Region** | **gpt-4o**, **2024-05-13** | **gpt-4o**, **2024-08-06** | **gpt-4o-mini**, **2024-07-18** | **gpt-4**, **0613** | **gpt-4**, **turbo-2024-04-09** | **gpt-35-turbo**, **0613** | **gpt-35-turbo**, **1106** | **gpt-35-turbo**, **0125** |
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|:-----------------|:--------------------------:|:--------------------------:|:-------------------------------:|:-------------------:|:-------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|
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| australiaeast | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| canadaeast | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| eastus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| eastus2 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| northcentralus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| southcentralus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| southindia | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| swedencentral | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| switzerlandnorth | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| uksouth | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| westus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| westus3 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| westus3 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |

‎articles/ai-services/openai/overview.md

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- Low detail allows the API to return faster responses for scenarios that don't require high image resolution analysis. The tokens consumed for low detail images are:
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- **GPT-4o and GPT-4 Turbo with Vision**: Flat rate of **85 tokens per image**, regardless of size.
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- **GPT-4o mini**: Flat rate of **2833 tokens per image**, regardless of size.
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- **Example: 4096 x 8192 image (low detail)**: The cost is a fixed 85 tokens, because it's a low detail image, and the size doesn't affect the cost in this mode.
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- **Example: 4096 x 8192 image (low detail)**: The cost is a fixed 85 tokens with GPT-4o, because it's a low detail image, and the size doesn't affect the cost in this mode.
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- **High resolution mode**
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- Low detail allows the API to analyze images in more detail. Image tokens are calculated based on the image's dimensions. The calculation involves the following steps:
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- High detail allows the API to analyze images in more detail. Image tokens are calculated based on the image's dimensions. The calculation involves the following steps:
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1. **Image resizing**: The image is resized to fit within a 2048 x 2048 pixel square. If the shortest side is larger than 768 pixels, the image is further resized so that the shortest side is 768 pixels long. The aspect ratio is preserved during resizing.
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1. **Tile calculation**: Once resized, the image is divided into 512 x 512 pixel tiles. Any partial tiles are rounded up to a full tile. The number of tiles determines the total token cost.
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1. **Token calculation**:
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## Next steps
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Learn more about the [underlying models that power Azure OpenAI](./concepts/models.md).
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Learn more about the [underlying models that power Azure OpenAI](./concepts/models.md).

‎articles/machine-learning/component-reference/feature-hashing.md

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title: "Feature Hashing component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the Feature Hashing component in the Azure Machine Learning designer to featurize text data.
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description: "[Deprecated] Learn how to use the Feature Hashing component in the Azure Machine Learning designer to featurize text data."
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services: machine-learning
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# Feature Hashing component reference
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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This article describes a component included in Azure Machine Learning designer.
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Use the Feature Hashing component to transform a stream of English text into a set of integer features. You can then pass this hashed feature set to a machine learning algorithm to train a text analytics model.

‎articles/machine-learning/component-reference/pca-based-anomaly-detection.md

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title: "PCA-Based Anomaly Detection: Component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the PCA-Based Anomaly Detection component to create an anomaly detection model based on principal component analysis (PCA).
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description: "[Deprecated] Learn how to use the PCA-Based Anomaly Detection component to create an anomaly detection model based on principal component analysis (PCA)."
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# PCA-Based Anomaly Detection component
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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This article describes how to use the PCA-Based Anomaly Detection component in Azure Machine Learning designer, to create an anomaly detection model based on principal component analysis (PCA).
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This component helps you build a model in scenarios where it's easy to get training data from one class, such as valid transactions, but difficult to get sufficient samples of the targeted anomalies.

‎articles/machine-learning/component-reference/poisson-regression.md

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title: "Poisson Regression: Component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the Poisson Regression component in Azure Machine Learning designer to create a Poisson regression model.
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description: "[Deprecated] Learn how to use the Poisson Regression component in Azure Machine Learning designer to create a Poisson regression model."
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# Poisson Regression
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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Use this component to create a Poisson regression model in a pipeline. Poisson regression is intended for predicting numeric values, typically counts. Therefore, you should use this component to create your regression model only if the values you are trying to predict fit the following conditions:

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