Merge pull request #674 from MicrosoftDocs/main

Publish to live, Monday 4 AM PST, 10/7

Author: ttorble

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

@@ -6,7 +6,7 @@ description: Learn more about how the Azure OpenAI embeddings API uses cosine si
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: tutorial
-ms.date: 09/05/2024
+ms.date: 10/6/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -15,7 +15,7 @@ ms.custom:
 
 # Understand embeddings in Azure OpenAI Service
 
-An embedding is a special format of data representation that machine learning models and algorithms can easily use. 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. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
+An embedding is a special format of data representation that machine learning models and algorithms can easily use. 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. Embeddings power vector similarity search in retrieval systems such as [Azure AI Search](/azure/search) (recommended) and in Azure databases such as [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search), and [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
 
 ## Embedding models
 

articles/machine-learning/concept-automl-forecasting-sweeping.md

@@ -66,7 +66,7 @@ AutoML follows the usual cross-validation procedure, training a separate model o
 
 Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds, and optionally, the number of time periods between two consecutive cross-validation folds. For more information and an example of configuring cross-validation for forecasting, see [Custom cross-validation settings](how-to-auto-train-forecast.md#custom-cross-validation-settings).
 
-You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-data).
+You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-dataset).
 
 ## Related content
 

articles/machine-learning/v1/concept-automated-ml.md

@@ -253,7 +253,7 @@ To help confirm that such bias isn't applied to the final recommended model, aut
 >[!IMPORTANT]
 > Testing your models with a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
 
-Learn how to [configure AutoML experiments to use test data (preview) with the SDK (v1)](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview) or with the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment).
+Learn how to [configure AutoML experiments to use test data (preview) with the SDK (v1)](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview) or with the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment).
 
 You can also [test any existing automated ML model (preview) (v1)](../how-to-configure-auto-train.md)), including models from child jobs, by providing your own test data or by setting aside a portion of your training data. 
 

articles/machine-learning/v1/how-to-auto-train-forecast.md

@@ -69,7 +69,7 @@ automl_config = AutoMLConfig(task='forecasting',
 ```
 
 
-You can also bring your own validation data, learn more in [Configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-data).
+You can also bring your own validation data, learn more in [Configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-dataset).
 
 Learn more about how AutoML applies cross validation to [prevent over-fitting models](../concept-manage-ml-pitfalls.md#prevent-overfitting).
 
@@ -295,7 +295,7 @@ Next, let's examine the image, which plots the original series in first differen
 
 AutoML Machine learning models cannot inherently deal with stochastic trends, or other well-known problems associated with non-stationary time series. As a result, their out of sample forecast accuracy is "poor" if such trends are present.
 
-AutoML automatically analyzes time series dataset to check whether it's stationary or not. When non-stationary time series are detected, AutoML applies a differencing transform automatically to mitigate the affect of non-stationary time series.
+AutoML automatically analyzes time series dataset to check whether it's stationary or not. When non-stationary time series are detected, AutoML applies a differencing transform automatically to mitigate the effect of non-stationary time series.
 
 ## Run the experiment 
 
@@ -412,7 +412,7 @@ mm_paramters = ManyModelsTrainParameters(automl_settings=automl_settings, partit
 
 ### Hierarchical time series forecasting
 
-In most applications, customers have a need to understand their forecasts at a macro and micro level of the business. Forcasts can be predicting sales of products at different geographic locations, or understanding the expected workforce demand for different organizations at a company. The ability to train a machine learning model to intelligently forecast on hierarchy data is essential. 
+In most applications, customers have a need to understand their forecasts at a macro and micro level of the business. Forecasts can be predicting sales of products at different geographic locations, or understanding the expected workforce demand for different organizations at a company. The ability to train a machine learning model to intelligently forecast on hierarchy data is essential. 
 
 A hierarchical time series is a structure in which each of the unique series is arranged into a hierarchy based on dimensions such as, geography or product type. The following example shows data with unique attributes that form a hierarchy. Our hierarchy is defined by: the product type such as headphones or tablets, the product category, which splits product types into accessories and devices, and the region the products are sold in. 
 

articles/machine-learning/v1/how-to-configure-auto-train.md

@@ -109,7 +109,7 @@ If you do not explicitly specify a `validation_data` or `n_cross_validation` par
 > [!TIP] 
 > You can upload **test data (preview)** to evaluate models that automated ML generated for you. These features are  [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview capabilities, and may change at any time.
 > Learn how to: 
-> * [Pass in test data to your AutoMLConfig object](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview). 
+> * [Pass in test data to your AutoMLConfig object](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview). 
 > * [Test the models automated ML generated for your experiment](#test-models-preview).
 >  
 > If you prefer a no-code experience, see [step 12 in Set up AutoML with the studio UI](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment)
@@ -449,7 +449,7 @@ RunDetails(run).show()
 >  * [Forecasting tasks where deep learning neural networks (DNN) are enabled](../how-to-auto-train-forecast.md#enable-deep-learning)
 >  * [Automated ML runs from local computes or Azure Databricks clusters](../how-to-configure-auto-train.md#compute-to-run-experiment)
 
-Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, automatically triggers a remote test run that uses the provided test data to evaluate the best model that automated ML recommends upon completion of the experiment. This remote test run is done at the end of the experiment, once the best model is determined. See how to [pass test data into your `AutoMLConfig`](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview). 
+Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, automatically triggers a remote test run that uses the provided test data to evaluate the best model that automated ML recommends upon completion of the experiment. This remote test run is done at the end of the experiment, once the best model is determined. See how to [pass test data into your `AutoMLConfig`](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview). 
 
 ### Get test job results