Merge pull request #228436 from shohei1029/patch-10

Use official name instead of abbreviations (AzureML/Azure ML -> Azure Machine Learning)

Author: 19BMG00

articles/machine-learning/azure-machine-learning-ci-image-release-notes.md

@@ -14,7 +14,7 @@ ms.topic: reference
 
 # Azure Machine Learning compute instance image release notes
 
-In this article, learn about Azure Machine Learning compute instance image releases. Azure Machine Learning maintains host operating system images for [Azure ML compute instance](./concept-compute-instance.md) and [Data Science Virtual Machines](./data-science-virtual-machine/release-notes.md). Due to the rapidly evolving needs and package updates, we target to release new images every month.
+In this article, learn about Azure Machine Learning compute instance image releases. Azure Machine Learning maintains host operating system images for [Azure Machine Learning compute instance](./concept-compute-instance.md) and [Data Science Virtual Machines](./data-science-virtual-machine/release-notes.md). Due to the rapidly evolving needs and package updates, we target to release new images every month.
 
 Azure Machine Learning checks and validates any machine learning packages that may require an upgrade. Updates incorporate the latest OS-related patches from Canonical as the original Linux OS publisher. In addition to patches applied by the original publisher, Azure Machine Learning updates system packages when updates are available. For details on the patching process, see [Vulnerability Management](./concept-vulnerability-management.md).
 
@@ -26,7 +26,7 @@ Version: `23.01.19`
 Main changes:
 
 - Added new conda environment `jupyter-env`
-- Moved jupyter service to new `jupyter-env` conda environment
+- Moved Jupyter service to new `jupyter-env` conda environment
 - `Azure Machine Learning SDK` to version `1.48.0`
 
 Main environment specific updates:

articles/machine-learning/azure-machine-learning-glossary.md

@@ -30,7 +30,7 @@ A compute is a designated compute resource where you run your job or host your e
 
 * **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
 * **Compute instance** - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
-* **Kubernetes cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure ML workspace, or attach an existing AKS cluster.
+* **Kubernetes cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure Machine Learning workspace, or attach an existing AKS cluster.
 * **Attached compute** - You can attach your own compute resources to your workspace and use them for training and inference.
 
 ## Data
@@ -67,19 +67,19 @@ Azure Machine Learning environments are an encapsulation of the environment wher
 
 ### Types of environment
 
-Azure ML supports two types of environments: curated and custom.
+Azure Machine Learning supports two types of environments: curated and custom.
 
 Curated environments are provided by Azure Machine Learning and are available in your workspace by default. Intended to be used as is, they contain collections of Python packages and settings to help you get started with various machine learning frameworks. These pre-created environments also allow for faster deployment time. For a full list, see the [curated environments article](resource-curated-environments.md).
 
-In custom environments, you're responsible for setting up your environment. Make sure to install the packages and any other dependencies that your training or scoring script needs on the compute. Azure ML allows you to create your own environment using
+In custom environments, you're responsible for setting up your environment. Make sure to install the packages and any other dependencies that your training or scoring script needs on the compute. Azure Machine Learning allows you to create your own environment using
 
 * A docker image
 * A base docker image with a conda YAML to customize further
 * A docker build context
 
 ## Model
 
-Azure machine learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure ML supports three types of storage format for models:
+Azure machine learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
 
 * `custom_model`
 * `mlflow_model`

articles/machine-learning/azure-machine-learning-release-notes-cli-v2.md

@@ -141,7 +141,7 @@ __RSS feed__: Get notified when this page is updated by copying and pasting the
 
 - `az ml job`
   - For all job types, flattened the `code` section of the YAML schema. Instead of `code.local_path` to specify the path to the source code directory, it is now just `code`
-  - For all job types, changed the schema for defining data inputs to the job in the job YAML. Instead of specifying the data path using either the `file` or `folder` fields, use the `path` field to specify either a local path, a URI to a cloud path containing the data, or a reference to an existing registered Azure ML data asset via `path: azureml:<data_name>:<data_version>`. Also specify the `type` field to clarify whether the data source is a single file (`uri_file`) or a folder (`uri_folder`). If `type` field is omitted, it defaults to `type: uri_folder`. For more information, see the section of any of the [job YAML references](reference-yaml-job-command.md) that discuss the schema for specifying input data.
+  - For all job types, changed the schema for defining data inputs to the job in the job YAML. Instead of specifying the data path using either the `file` or `folder` fields, use the `path` field to specify either a local path, a URI to a cloud path containing the data, or a reference to an existing registered Azure Machine Learning data asset via `path: azureml:<data_name>:<data_version>`. Also specify the `type` field to clarify whether the data source is a single file (`uri_file`) or a folder (`uri_folder`). If `type` field is omitted, it defaults to `type: uri_folder`. For more information, see the section of any of the [job YAML references](reference-yaml-job-command.md) that discuss the schema for specifying input data.
   - In the [sweep job YAML schema](reference-yaml-job-sweep.md), changed the `sampling_algorithm` field from a string to an object in order to support additional configurations for the random sampling algorithm type
   - Removed the component job YAML schema. With this release, if you want to run a command job inside a pipeline that uses a component, just specify the component to the `component` field of the command job YAML definition. 
   - For all job types, added support for referencing the latest version of a nested asset in the job YAML configuration. When referencing a registered environment or data asset to use as input in a job, you can alias by latest version rather than having to explicitly specify the version. For example: `environment: azureml:AzureML-Minimal@latest`
@@ -186,7 +186,7 @@ __RSS feed__: Get notified when this page is updated by copying and pasting the
   - Added support for running pipeline jobs ([pipeline job YAML schema](reference-yaml-job-pipeline.md))
   - Added support for job input literals and input data URIs for all job types
   - Added support for job outputs for all job types
-  - Changed the expression syntax from `{ <expression> }` to `${{ <expression> }}`. For more information, see [Expression syntax for configuring Azure ML jobs](reference-yaml-core-syntax.md#expression-syntax-for-configuring-azure-ml-jobs-and-components)
+  - Changed the expression syntax from `{ <expression> }` to `${{ <expression> }}`. For more information, see [Expression syntax for configuring Azure Machine Learning jobs](reference-yaml-core-syntax.md#expression-syntax-for-configuring-azure-machine-learning-jobs-and-components)
 - `az ml environment`
   - Updated [environment YAML schema](reference-yaml-environment.md)
   - Added support for creating environments from Docker build context
@@ -197,7 +197,7 @@ __RSS feed__: Get notified when this page is updated by copying and pasting the
   - Renamed `az ml data` subgroup to `az ml dataset`
   - Updated dataset YAML schema
 - `az ml component`
-  - Added the `az ml component` commands for managing Azure ML components
+  - Added the `az ml component` commands for managing Azure Machine Learning components
   - Added support for command components ([command component YAML schema](reference-yaml-component-command.md))
 - `az ml online-endpoint`
   - `az ml endpoint` subgroup split into two separate groups: `az ml online-endpoint` and `az ml batch-endpoint`

articles/machine-learning/component-reference-v2/component-reference-v2.md

@@ -18,7 +18,7 @@ Azure Machine Learning designer components (Designer) allow users to create mach
 
 This reference content provides background on each of the custom components (v2) available in Azure Machine Learning designer.
 
-You can navigate to Custom components in AzureML Studio as shown in the following image.
+You can navigate to Custom components in Azure Machine Learning Studio as shown in the following image.
 
 :::image type="content" source="media/designer-new-pipeline.png" alt-text="Diagram showing the Designer UI for selecting a custom component.":::
 

articles/machine-learning/component-reference/convert-to-csv.md

@@ -28,7 +28,7 @@ Even if you do most of your work in Azure Machine Learning, there are times when
 + Save the CSV file to cloud storage and connect to it from Power BI to create visualizations.  
 + Use the CSV format to prepare data for use in R and Python. 
 
-When you convert a dataset to CSV, the csv is saved in your Azure ML workspace. You can use an Azure storage utility to open and use the file directly. You can also access the CSV in the designer by selecting the **Convert to CSV** component, then select the histogram icon under the **Outputs** tab in the right panel to view the output. You can download the CSV from the Results folder to a local directory.  
+When you convert a dataset to CSV, the csv is saved in your Azure Machine Learning workspace. You can use an Azure storage utility to open and use the file directly. You can also access the CSV in the designer by selecting the **Convert to CSV** component, then select the histogram icon under the **Outputs** tab in the right panel to view the output. You can download the CSV from the Results folder to a local directory.  
 
 ## How to configure Convert to CSV
 
@@ -44,7 +44,7 @@ When you convert a dataset to CSV, the csv is saved in your Azure ML workspace.
 
 Select the **Outputs** tab in the right panel of **Convert to CSV**, and select on one of these icons under the **Port outputs**.  
 
-+ **Register dataset**: Select the icon and save the CSV file back to the Azure ML workspace as a separate dataset. You can find the dataset as a component in the component tree under the **My Datasets** section.
++ **Register dataset**: Select the icon and save the CSV file back to the Azure Machine Learning workspace as a separate dataset. You can find the dataset as a component in the component tree under the **My Datasets** section.
 
  + **View output**: Select the eye icon, and follow the instruction to browse the **Results_dataset** folder, and download the data.csv file.
 

articles/machine-learning/component-reference/create-python-model.md

@@ -47,7 +47,7 @@ This article shows how to use **Create Python Model** with a simple pipeline. He
 
    ```Python
 
-   # The script MUST define a class named AzureMLModel.
+   # The script MUST define a class named Azure Machine LearningModel.
    # This class MUST at least define the following three methods:
        # __init__: in which self.model must be assigned,
        # train: which trains self.model, the two input arguments must be pandas DataFrame,

articles/machine-learning/component-reference/designer-error-codes.md

@@ -1153,7 +1153,7 @@ See the following articles for help with Hive queries for machine learning:
 **Resolution:**
  Revisit the component and inspect the SQL query for mistakes.  
 
- Verify that the query works correctly outside of Azure ML by logging in to the database server directly and running the query.  
+ Verify that the query works correctly outside of Azure Machine Learning by logging in to the database server directly and running the query.  
 
  If there is a SQL generated message reported by the component exception, take action based on the reported error. For example, the error messages sometimes include specific guidance on the likely error:
 + *No such column or missing database*, indicating that you might have typed a column name wrong. If you are sure the column name is correct, try using brackets or quotation marks to enclose the column identifier.
@@ -1554,7 +1554,7 @@ Resolution:
 |Exception Messages|
 |------------------------|
 |Datastore information is invalid.|
-|Datastore information is invalid. Failed to get AzureML datastore '{datastore_name}' in workspace '{workspace_name}'.|
+|Datastore information is invalid. Failed to get Azure Machine Learning datastore '{datastore_name}' in workspace '{workspace_name}'.|
 
 
 ## Error 0158

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

@@ -33,9 +33,9 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
 
 1. **Identify the ML problem** to be solved: classification, forecasting, regression, computer vision or NLP.
 
-1. **Choose whether you want a code-first experience or a no-code studio web experience**: Users who prefer a code-first experience can use the [AzureML SDKv2](how-to-configure-auto-train.md) or the [AzureML CLIv2](how-to-train-cli.md). Get started with [Tutorial: Train an object detection model with AutoML and Python](tutorial-auto-train-image-models.md). Users who prefer a limited/no-code experience can use the [web interface](how-to-use-automated-ml-for-ml-models.md) in Azure Machine Learning studio at [https://ml.azure.com](https://ml.azure.com/).  Get started with [Tutorial: Create a classification model with automated ML in Azure Machine Learning](tutorial-first-experiment-automated-ml.md).
+1. **Choose whether you want a code-first experience or a no-code studio web experience**: Users who prefer a code-first experience can use the [Azure Machine Learning SDKv2](how-to-configure-auto-train.md) or the [Azure Machine Learning CLIv2](how-to-train-cli.md). Get started with [Tutorial: Train an object detection model with AutoML and Python](tutorial-auto-train-image-models.md). Users who prefer a limited/no-code experience can use the [web interface](how-to-use-automated-ml-for-ml-models.md) in Azure Machine Learning studio at [https://ml.azure.com](https://ml.azure.com/).  Get started with [Tutorial: Create a classification model with automated ML in Azure Machine Learning](tutorial-first-experiment-automated-ml.md).
 
-1. **Specify the source of the labeled training data**: You can bring your data to AzureML in [many different ways](concept-data.md).
+1. **Specify the source of the labeled training data**: You can bring your data to Azure Machine Learning in [many different ways](concept-data.md).
 
 1. **Configure the automated machine learning parameters** that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.  
 1. **Submit the training job.**
@@ -70,7 +70,7 @@ See an example of classification and automated machine learning in this Python n
 
 ### Regression
 
-Similar to classification, regression tasks are also a common supervised learning task. AzureML offers featurization specific to regression problems. Learn more about [featurization options](how-to-configure-auto-train.md#data-featurization). You can also find the list of algorithms supported by AutoML [here](how-to-configure-auto-train.md#supported-algorithms). 
+Similar to classification, regression tasks are also a common supervised learning task. Azure Machine Learning offers featurization specific to regression problems. Learn more about [featurization options](how-to-configure-auto-train.md#data-featurization). You can also find the list of algorithms supported by AutoML [here](how-to-configure-auto-train.md#supported-algorithms). 
 
 Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating, etc. 
 
@@ -105,7 +105,7 @@ With this capability you can:
 * Download or deploy the resulting model as a web service in Azure Machine Learning. 
 * Operationalize at scale, leveraging Azure Machine Learning [MLOps](concept-model-management-and-deployment.md) and [ML Pipelines](concept-ml-pipelines.md) capabilities. 
 
-Authoring AutoML models for vision tasks is supported via the Azure ML Python SDK. The resulting experimentation jobs, models, and outputs can be accessed from the Azure Machine Learning studio UI.
+Authoring AutoML models for vision tasks is supported via the Azure Machine Learning Python SDK. The resulting experimentation jobs, models, and outputs can be accessed from the Azure Machine Learning studio UI.
 
 Learn how to [set up AutoML training for computer vision models](how-to-auto-train-image-models.md).
 

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

@@ -137,7 +137,7 @@ Lags of feature columns | Optional
 Rolling window aggregations (for example, rolling average) of target quantity | Optional
 Seasonal decomposition ([STL](https://otexts.com/fpp3/stl.html)) | Optional
 
-You can configure featurization from the AutoML SDK via the [ForecastingJob](/python/api/azure-ai-ml/azure.ai.ml.automl.forecastingjob#azure-ai-ml-automl-forecastingjob-set-forecast-settings) class or from the [AzureML Studio web interface](how-to-use-automated-ml-for-ml-models.md#customize-featurization).
+You can configure featurization from the AutoML SDK via the [ForecastingJob](/python/api/azure-ai-ml/azure.ai.ml.automl.forecastingjob#azure-ai-ml-automl-forecastingjob-set-forecast-settings) class or from the [Azure Machine Learning Studio web interface](how-to-use-automated-ml-for-ml-models.md#customize-featurization).
 
 ### Non-stationary time series detection and handling