Fix typo: programatically -> programmatically

Author: changeworld

articles/ai-services/agents/how-to/tools/bing-grounding.md

@@ -92,4 +92,4 @@ print(f"Last run step detail: {run_steps_data}")
 
 ## Next steps
 
-See [code samples](./bing-code-samples.md) for using the Grounding with Bing tool programatically.
+See [code samples](./bing-code-samples.md) for using the Grounding with Bing tool programmatically.

articles/ai-services/agents/how-to/tools/code-interpreter-samples.md

@@ -21,7 +21,7 @@ Azure AI Agents supports using the Code Interpreter tool, which allows an agent
 
 ## Using the code interpreter tool with an agent
 
-You can add the code interpreter tool to an agent programatically using the code examples listed at the top of this article, or the [Azure AI Foundry portal](https://ai.azure.com/). If you want to use the portal:
+You can add the code interpreter tool to an agent programmatically using the code examples listed at the top of this article, or the [Azure AI Foundry portal](https://ai.azure.com/). If you want to use the portal:
 
 1. In the **Agents** screen for your agent, scroll down the **Setup** pane on the right to **action**. Then select **Add**.
 

articles/ai-services/agents/how-to/tools/fabric.md

@@ -44,7 +44,7 @@ You need to first build and publish a Fabric data agent and then connect your Fa
 
 :::zone pivot="portal"
 
-You can add the Microsoft Fabric tool to an agent programatically using the code examples listed at the top of this article, or the Azure AI Foundry portal. If you want to use the portal: 
+You can add the Microsoft Fabric tool to an agent programmatically using the code examples listed at the top of this article, or the Azure AI Foundry portal. If you want to use the portal: 
 
 1. Navigate to the **Agents** screen for your agent in [Azure AI Foundry](https://ai.azure.com/), scroll down the Setup pane on the right to **knowledge**. Then select **Add**.
 

articles/ai-services/openai/includes/batch/batch-python.md

@@ -226,7 +226,7 @@ If your batch jobs are so large that you're hitting the enqueued token limit eve
 
 ## Track batch job progress
 
-Once you have created batch job successfully you can monitor its progress either in the Studio or programatically. When checking batch job progress we recommend waiting at least 60 seconds in between each status call.
+Once you have created batch job successfully you can monitor its progress either in the Studio or programmatically. When checking batch job progress we recommend waiting at least 60 seconds in between each status call.
 
 ```Python
 import time

articles/ai-services/openai/includes/batch/batch-rest.md

@@ -189,7 +189,7 @@ The default 500 max file limit per resource also applies to output files. Here y
 
 ## Track batch job progress
 
-Once you have created batch job successfully you can monitor its progress either in the Studio or programatically. When checking batch job progress we recommend waiting at least 60 seconds in between each status call.
+Once you have created batch job successfully you can monitor its progress either in the Studio or programmatically. When checking batch job progress we recommend waiting at least 60 seconds in between each status call.
 
 ```http
 curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/batches/{batch_id}?api-version=2025-03-01-preview \

articles/machine-learning/how-to-use-mlflow-cli-runs.md

@@ -204,7 +204,7 @@ mlflow.autolog()
 
 ## View metrics and artifacts in your workspace
 
-The metrics and artifacts from MLflow logging are tracked in your workspace. You can view and access them in Azure Machine Learning studio or access them programatically via the MLflow SDK.
+The metrics and artifacts from MLflow logging are tracked in your workspace. You can view and access them in Azure Machine Learning studio or access them programmatically via the MLflow SDK.
 
 To view metrics and artifacts in the studio:
 
@@ -215,7 +215,7 @@ To view metrics and artifacts in the studio:
 
    :::image type="content" source="media/how-to-log-view-metrics/metrics.png" alt-text="Screenshot of the metrics view that shows the list of metrics and the charts created from the metrics." lightbox="media/how-to-log-view-metrics/metrics.png"::: 
 
-To access or query metrics, parameters, and artifacts programatically via the MLflow SDK, use [mlflow.get_run()](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.get_run).
+To access or query metrics, parameters, and artifacts programmatically via the MLflow SDK, use [mlflow.get_run()](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.get_run).
 
 ```python
 import mlflow

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -234,7 +234,7 @@ When you deploy a trained model in the designer, you can [deploy the model as a
 
 #### Pipeline endpoints
 
-Pipeline endpoints let you call your [ML Pipelines](#ml-pipelines) programatically via a REST endpoint. Pipeline endpoints let you automate your pipeline workflows.
+Pipeline endpoints let you call your [ML Pipelines](#ml-pipelines) programmatically via a REST endpoint. Pipeline endpoints let you automate your pipeline workflows.
 
 A pipeline endpoint is a collection of published pipelines. This logical organization lets you manage and call multiple pipelines using the same endpoint. Each published pipeline in a pipeline endpoint is versioned. You can select a default pipeline for the endpoint, or specify a version in the REST call.
 

articles/machine-learning/v1/how-to-designer-import-data.md

@@ -31,7 +31,7 @@ We recommend that you use [datasets](concept-data.md) to import data into the de
 
 ### Register a dataset
 
-You can register existing datasets [programatically with the SDK](how-to-create-register-datasets.md#create-datasets-from-datastores) or [visually in Azure Machine Learning studio](how-to-connect-data-ui.md#create-data-assets).
+You can register existing datasets [programmatically with the SDK](how-to-create-register-datasets.md#create-datasets-from-datastores) or [visually in Azure Machine Learning studio](how-to-connect-data-ui.md#create-data-assets).
 
 You can also register the output for any designer component as a dataset.
 

articles/search/includes/quickstarts/full-text-javascript.md

@@ -593,7 +593,7 @@ console.log(`Index named ${index.name} has been created.`);
 
 ### Load documents 
 
-In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this case, we'll programatically push the documents to the index.
+In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this case, we'll programmatically push the documents to the index.
 
 Document inputs might be rows in a database, blobs in Blob storage, or, as in this sample, JSON documents on disk. Similar to what we did with the `indexDefinition`, we also need to import `hotels.json` at the top of *index.js* so that the data can be accessed in our main function.
 

articles/search/includes/quickstarts/full-text-typescript.md

@@ -529,7 +529,7 @@ console.log(`Index named ${index.name} has been created.`);
 
 ### Load documents 
 
-In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this case, we'll programatically push the documents to the index.
+In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this case, we'll programmatically push the documents to the index.
 
 Document inputs might be rows in a database, blobs in Blob storage, or, as in this sample, JSON documents on disk. You can either download [hotels.json](https://github.com/Azure-Samples/azure-search-javascript-samples/blob/main/quickstart/hotels.json) or create your own *hotels.json* file with the following content: