Merge pull request #230617 from MicrosoftDocs/main

3/14 OOB Publish at 8AM

Author: huypub

articles/app-service/configure-common.md

@@ -394,7 +394,7 @@ az webapp config connection-string list --resource-group <group-name> --name <ap
 ...
 
 # Update the app with the JSON file
-az webapp config appsettings set --resource-group <group-name> --name <app-name> --settings @settings.json
+az webapp config connection-string set --resource-group <group-name> --name <app-name> --settings @settings.json
 ```
 
 # [Azure PowerShell](#tab/ps)

articles/azure-functions/functions-add-output-binding-storage-queue-vs-code.md

@@ -56,7 +56,7 @@ This article assumes that you're already signed in to your Azure subscription fr
 
 In the [previous quickstart article](./create-first-function-vs-code-csharp.md), you created a function app in Azure along with the required storage account. The connection string for this account is stored securely in the app settings in Azure. In this article, you write messages to a Storage queue in the same account. To connect to your storage account when running the function locally, you must download app settings to the *local.settings.json* file.
 
-1. Press <kbd>F1</kbd> to open the command palette, then search for and run the command `Azure Functions: Download Remote Settings....`.
+1. Press <kbd>F1</kbd> to open the command palette, then search for and run the command `Azure Functions: Download Remote Settings...`.
 
 1. Choose the function app you created in the previous article. Select **Yes to all** to overwrite the existing local settings.
 

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

@@ -164,37 +164,37 @@ When using our embeddings models, keep in mind their limitations and risks.
 
 |  Model ID  | Supports Completions | Supports Embeddings |  Base model Regions   | Fine-Tuning Regions | Max Request (tokens) | Training Data (up to)  |
 |  --------- | -------------------- | ------------------- | --------------------- | ------------------- | -------------------- | ---------------------- |
-| ada | 	Yes	| No	|	N/A	| East US<sup>2</sup>, South Central US, West Europe | 2,049 | Oct 2019|
-| text-ada-001 | Yes | No | East US<sup>2</sup>, South Central US, West Europe | N/A | 2,049 | Oct 2019|
-| babbage | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe | 2,049 | Oct 2019 |
-| text-babbage-001 | Yes | No | East US<sup>2</sup>, South Central US, West Europe | N/A | 2,049 | Oct 2019 |
-| curie | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe | 2,049 | Oct 2019 |
-| text-curie-001 | Yes | No | East US<sup>2</sup>, South Central US, West Europe | N/A | 2,049 | Oct 2019 |
-| davinci<sup>1</sup> | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe | 2,049 | Oct 2019|
+| ada | 	Yes	| No	|	N/A	| East US<sup>2</sup>, South Central US, West Europe<sup>2</sup> | 2,049 | Oct 2019|
+| text-ada-001 | Yes | No | East US, South Central US, West Europe | N/A | 2,049 | Oct 2019|
+| babbage | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe<sup>2</sup> | 2,049 | Oct 2019 |
+| text-babbage-001 | Yes | No | East US, South Central US, West Europe | N/A | 2,049 | Oct 2019 |
+| curie | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe<sup>2</sup> | 2,049 | Oct 2019 |
+| text-curie-001 | Yes | No | East US, South Central US, West Europe | N/A | 2,049 | Oct 2019 |
+| davinci<sup>1</sup> | Yes | No | N/A | East US<sup>2</sup>, South Central US, West Europe<sup>2</sup> | 2,049 | Oct 2019|
 | text-davinci-001 | Yes | No | South Central US, West Europe | N/A |  |  |
 | text-davinci-002 | Yes | No | East US, South Central US, West Europe | N/A | 4,097 | Jun 2021 |
 | text-davinci-003 | Yes | No | East US | N/A | 4,097 | Jun 2021 |
-| text-davinci-fine-tune-002<sup>1</sup> | Yes | No | N/A | East US, West Europe |  |  |
+| text-davinci-fine-tune-002<sup>1</sup> | Yes | No | N/A | East US, West Europe<sup>2</sup> |  |  |
 | gpt-35-turbo<sup>3</sup> (ChatGPT) | Yes | No | N/A | East US, South Central US | 4,096 | Sep 2021
 
 <sup>1</sup> The model is available by request only. Currently we aren't accepting new requests to use the model.
-<br><sup>2</sup> East US is currently unavailable for new customers to fine-tune due to high demand. Please use US South Central region for US based training.
+<br><sup>2</sup> East US and West Europe are currently unavailable for new customers to fine-tune due to high demand. Please use US South Central region for fine-tuning. 
 <br><sup>3</sup> Currently, only version `"0301"` of this model is available. This version of the model will be deprecated on 8/1/2023 in favor of newer version of the gpt-35-model. See [ChatGPT model versioning](../how-to/chatgpt.md#model-versioning) for more details.
 
 ### Codex Models
 |  Model ID  | Supports Completions | Supports Embeddings |  Base model Regions   | Fine-Tuning Regions | Max Request (tokens) | Training Data (up to)  |
 |  --- | --- | --- | --- | --- | --- | --- |
-| code-cushman-001<sup>1</sup> | Yes | No | South Central US, West Europe | East US<sup>2</sup> , South Central US, West Europe | 2,048 | |
+| code-cushman-001<sup>1</sup> | Yes | No | South Central US, West Europe | East US<sup>2</sup> , South Central US, West Europe<sup>2</sup> | 2,048 | |
 | code-davinci-002 | Yes | No | East US,  West Europe |  N/A | 8,001 | Jun 2021 |
-| code-davinci-fine-tune-002<sup>1</sup> | Yes | No | N/A | East US<sup>2</sup> , West Europe | | |
+| code-davinci-fine-tune-002<sup>1</sup> | Yes | No | N/A | East US<sup>2</sup> , West Europe<sup>2</sup> | | |
 
 <sup>1</sup> The model is available for fine-tuning by request only. Currently we aren't accepting new requests to fine-tune the model.
 <br><sup>2</sup> East US is currently unavailable for new customers to fine-tune due to high demand. Please use US South Central region for US based training.
 
 ### Embeddings Models
 |  Model ID  | Supports Completions | Supports Embeddings |  Base model Regions   | Fine-Tuning Regions | Max Request (tokens) | Training Data (up to)  |
 |  --- | --- | --- | --- | --- | --- | --- |
-| text-ada-embeddings-002 | No | Yes | East US, South Central US, West Europe | N/A | 8,191 | Sep 2021 |
+| text-ada-embeddings-002 | No | Yes | East US, South Central US, West Europe | N/A |2,046 | Sep 2021 |
 | text-similarity-ada-001 | No | Yes | East US, South Central US, West Europe | N/A | 2,046 | Aug 2020 |
 | text-similarity-babbage-001 | No | Yes | South Central US, West Europe | N/A | 2,046 | Aug 2020 |
 | text-similarity-curie-001 | No | Yes | East US, South Central US, West Europe | N/A |  2046 | Aug 2020 |

articles/cognitive-services/openai/quotas-limits.md

@@ -32,8 +32,8 @@ The following sections provide you with a quick guide to the quotas and limits t
 | Max training jobs queued | 20 | 
 | Max Files per resource | 50 |
 | Total size of all files per resource | 1 GB | 
-| Max training job time (job will fail if exceeded) | 120 hours |
-| Max training job size (tokens in training file) x (# of epochs) | **Ada**: 40-M tokens <br> **Babbage**: 40-M tokens <br> **Curie**: 40-M tokens <br> **Cushman**: 40-M tokens <br> **Davinci**: 10-M |
+| Max training job time (job will fail if exceeded) | 720 hours |
+| Max training job size (tokens in training file) x (# of epochs) | 2 Billion |
 
 *The limits are subject to change. We anticipate that you will need higher limits as you move toward production and your solution scales. When you know your solution requirements, please reach out to us by applying for a quota increase here: <https://aka.ms/oai/quotaincrease>
 

articles/cognitive-services/openai/whats-new.md

@@ -19,6 +19,7 @@ keywords:
 
 - **ChatGPT (gpt-35-turbo) public preview**. To learn more checkout the new [quickstart](./quickstart.md), and [how-to articles](./how-to/chatgpt.md).
 
+- Increased training limits for fine-tuning: The max training job size (tokens in training file) x (# of epochs) is 2 Billion tokens for all models. We have also increased the max training job from 120 to 720 hours. 
 - Adding additional use cases to your existing access.  Previously, the process for adding new use cases required customers to reapply to the service. Now, we're releasing a new process that allows you to quickly add new use cases to your use of the service. This process follows the established Limited Access process within Azure Cognitive Services. [Existing customers can attest to any and all new use cases here](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUM003VEJPRjRSOTZBRVZBV1E5N1lWMk1XUyQlQCN0PWcu). Please note that this is required anytime you would like to use the service for a new use case you did not originally apply for.
 
 ## February 2023

articles/container-registry/tutorial-registry-cache.md

@@ -57,7 +57,7 @@ Implementing Caching for ACR provides the following benefits:
 
 - Quarantine functions like signing, scanning, and manual compliance approval are on the roadmap but not included in this release.
 
-- Caching will only occur after at least one pull request is complete on the available container image. For every new image available, a new pull request must be complete. Caching for ACR does not automatically pull new versions of images when a new version is available. It is on the roadmap but not supported in this release. 
+- Caching will only occur after at least one image pull request is complete on the available container image. For every new image available, a new image pull request must be complete. Caching for ACR does not automatically pull new versions of images when a new version is available. It is on the roadmap but not supported in this release. 
 
 -  Caching for ACR only supports Docker Hub and Microsoft Artifact Registry. Multiple other registries  including self-hosted registries are on the roadmap but aren't included in this release.
 
@@ -71,4 +71,4 @@ Implementing Caching for ACR provides the following benefits:
 
 <!-- LINKS - External -->
 
-[docker-rate-limit]:https://aka.ms/docker-rate-limit
+[docker-rate-limit]:https://aka.ms/docker-rate-limit

articles/data-factory/connector-hive.md

@@ -236,6 +236,9 @@ source(
 
 * Complex types such as arrays, maps, structs, and unions are not supported for read. 
 * Hive connector only supports Hive tables in Azure HDInsight of version 4.0 or greater (Apache Hive 3.1.0)
+* By default, Hive driver provides "tableName.columnName" in sink. If you do not wish to see the table name in the column name, then there are two ways to fix this.
+  a. Check the setting "hive.resultset.use.unique.column.names" in Hive server side and set it to false.
+  b. Use column mapping to rename the column name.
 
 ## Lookup activity properties
 

articles/data-manager-for-agri/concepts-hierarchy-model.md

@@ -0,0 +1,63 @@
+---
+title: Hierarchy model in Azure Data Manager for Agriculture 
+description: Provides information on the data model to organize your agriculture data. 
+author: gourdsay #Required; your GitHub user alias, with correct capitalization.
+ms.author: angour
+ms.service: data-manager-for-agri
+ms.topic: conceptual #Required; leave this attribute/value as-is.
+ms.date: 02/14/2023
+ms.custom: template-concept #Required; leave this attribute/value as-is.
+---
+
+# Hierarchy model to organize agriculture related data
+
+> [!NOTE]
+> Microsoft Azure Data Manager for Agriculture is currently in preview. For legal terms that apply to features that are in beta, in preview, or otherwise not yet released into general availability, see the [**Supplemental Terms of Use for Microsoft Azure Previews**](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
+> Microsoft Azure Data Manager for Agriculture requires registration and is available to only approved customers and partners during the preview period. To request access to Microsoft Data Manager for Agriculture during the preview period, use this [**form**](https://aka.ms/agridatamanager).
+
+To generate actionable insights data related to growers, farms, and fields should be organized in a well defined manner. Firms operating in the agriculture industry often perform longitudinal studies and need high quality data to generate insights. Data Manager for Agriculture  organizes agronomic data in the below manner.
+
+>:::image type="content" source="./media/data-model.png" alt-text="Screenshot showing farm hierarchy model.":::
+
+## Understanding farm hierarchy
+
+### Party  
+* Party is the owner and custodian of any data related to their farm. You could imagine Party to be the legal entity that is running the business.  
+* The onus of defining the Party entity is with the customer setting up Data Manager for Agriculture. 
+
+### Farm
+* Farms are logical entities. A farm is a collection of fields. 
+* Farms don't have any geometry associated with them. Farm entity helps you organize your growing operations. For example Contoso Inc is the Party that has farms in Oregon and Idaho.
+
+### Field
+* Fields denote a stable boundary that is in general agnostic to seasons and other temporal constructs. For example, field could be the boundary denoted in government records.
+* Fields are multi-polygon. For example, a road might divide the farm in two or more parts.
+* Fields are multi-boundary.
+
+### Seasonal field
+* This is the most important construct in the farming world. A seasonal fields definition includes the following things
+     * Boundary
+     * Season 
+     * Crop
+* A seasonal field is associated with a field or a farm
+* In Data Manager for Agriculture, seasonal fields are mono crop entities. In cases where farmers are cultivating different crops simultaneously, they have to create one seasonal field per crop.
+* A seasonal field is associated with one season. If a farmer cultivates across multiple seasons, they have to create one seasonal field per season.
+* It's multi-polygon. Same crop can be planted in different areas within the farm.
+
+
+### Boundary
+* Boundary represents the geometry of a field or a seasonal field.
+* It's represented as a multi-polygon GeoJSON consisting of vertices (lat/long).
+
+### Season
+* Season represents the temporal aspect of farming. It is a function of local agronomic practices, procedures and weather.
+
+### Crop
+* Crop entity provides the phenotypic details of the planted crop.
+
+### Crop product
+* Crop Product entity refers to the commercial variety (brand, product) of the planted seeds. A seasonal field can contain information about various varieties of seeds planted (belonging to the same crop).
+
+## Next steps
+
+* Test our APIs [here](/rest/api/data-manager-for-agri).

articles/data-manager-for-agri/concepts-ingest-satellite-imagery.md

@@ -0,0 +1,70 @@
+---
+title: Ingesting satellite data in Azure Data Manager for Agriculture
+description: Provides step by step guidance to ingest Satellite data
+author: gourdsay #Required; your GitHub user alias, with correct capitalization.
+ms.author: angour
+ms.service: data-manager-for-agri
+ms.topic: conceptual #Required; leave this attribute/value as-is.
+ms.date: 02/14/2023
+ms.custom: template-concept #Required; leave this attribute/value as-is.
+---
+
+# Using satellite imagery in Azure Data Manager for Agriculture 
+Our data manager  supports geospatial and temporal data. Remote sensing satellite imagery (which is geospatial and temporal) has huge applications in the field of agriculture. Farmers, agronomists and data scientists use of satellite imagery extensively to generate insights. Using satellite data in Data Manager for agriculture involves following steps.
+
+> [!NOTE]
+> Microsoft Azure Data Manager for Agriculture is currently in preview. For legal terms that apply to features that are in beta, in preview, or otherwise not yet released into general availability, see the [**Supplemental Terms of Use for Microsoft Azure Previews**](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
+> Microsoft Azure Data Manager for Agriculture requires registration and is available to only approved customers and partners during the preview period. To request access to Microsoft Data Manager for Agriculture during the preview period, use this [**form**](https://aka.ms/agridatamanager).
+
+>:::image type="content" source="./media/satellite-flow.png" alt-text="Diagram showing satellite data ingestion flow..":::
+
+## Satellite sources supported by Azure Data Manager for Agriculture
+In our public preview, we support ingesting data from Sentinel-2 constellation.
+
+## Sentinel-2
+[Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is a satellite constellation launched by 'European Space Agency' (ESA) under the Copernicus mission. This constellation has a pair of satellites and carries a Multi-Spectral Instrument (MSI) payload that samples 13 spectral bands: four bands at 10 m, six bands at 20 m and three bands at 60 m spatial resolution.  
+
+> [!Tip]
+> Sentinel-2 has two products: Level 1 (top of the atmosphere) data and its atmospherically corrected variant Level 2 (bottom of the atmosphere) data. We support ingesting and retrieving Level 1 and Level 2 data from Sentinel 2.
+
+## Image names and resolutions
+The image names and resolutions that are supported by APIs used to ingest and read satellite data (for Sentinel-2) in our service:
+
+| Category | Image Name |	Description |	Native resolution |
+|:-----:|:----:|:----:|:----:|
+|Raw bands| B01 |	Coastal aerosol | 60 m |
+|Raw bands| B02 |	Blue| 10 m |
+|Raw bands| B03 |	Green	| 10 m |
+|Raw bands| B04 |	Red	| 10 m |
+|Raw bands| B05 |	Vegetation red edge | 20 m |
+|Raw bands| B06 |	Vegetation red edge	| 20 m |
+|Raw bands| B07 |	Vegetation red edge	| 20 m |
+|Raw bands| B08 |	NIR	| 10 m |
+|Raw bands| B8A |	Narrow NIR | 20 m |
+|Raw bands| B09 |	Water vapor | 60 m |
+|Raw bands| B11 |	SWIR | 20 m |
+|Raw bands| B12 |	SWIR | 20 m |
+|Sen2Cor processor output| AOT |	Aerosol optical thickness map	| 10 m |
+|Sen2Cor processor output| SCL |	Scene classification data	| 20 m |
+|Sen2Cor processor output| SNW |	Snow probability| 20 m |
+|Sen2Cor processor output| CLD |	Cloud probability| 20 m |
+|Derived Indices| NDVI |	Normalized difference vegetation index | 10 m/20 m/60 m (user defined) |
+|Derived Indices| NDWI |	Normalized difference water index | 10 m/20 m/60 m (user defined) |
+|Derived Indices| EVI | Enhanced vegetation index | 10 m/20 m/60 m (user defined) |
+|Derived Indices| LAI | Leaf Area Index | 10 m/20 m/60 m (user defined) |
+|Derived Indices| LAIMask | Leaf Area Index Mask | 10 m/20 m/60 m (user defined) |
+|CLP|	Cloud probability, based on [s2cloudless](https://github.com/sentinel-hub/sentinel2-cloud-detector). | Values range from 0 (no clouds) to 255 (clouds). | 10 m/20 m/60 m  (user defined)|
+|CLM|	Cloud masks based on [s2cloudless](https://github.com/sentinel-hub/sentinel2-cloud-detector) | Value of 1 represents clouds, 0 represents no clouds and 255 represents no data. | 10 m/20 m/60 m  (user defined)|
+|dataMask	| Binary mask to denote availability of data | 0 represents non availability of data OR pixels lying outside the 'Area of interest' | Not applicable, per pixel value|
+
+## Points to note
+* We use CRS EPSG: 4326 for Sentinel-2 data. The resolutions quoted in the APIs are at the equator.
+* For preview:
+    * A maximum of five satellite jobs can be run concurrently, per tenant.
+    * A satellite job can ingest data for a maximum of one year in a single API call.
+    * Only TIFs are supported.
+    * Only 10 m, 20 m and 60 m images are supported.
+
+## Next steps
+
+* Test our APIs [here](/rest/api/data-manager-for-agri).