Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into us316607c

Author: TimShererWithAquent

articles/ai-studio/concepts/deployments-overview.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-studio
 ms.custom:
   - ignite-2023
   - build-2024
-ms.topic: conceptual
+ms.topic: concept-article
 ms.date: 5/21/2024
 ms.reviewer: fasantia
 ms.author: mopeakande
@@ -96,4 +96,4 @@ Optimizing LLMs requires a careful consideration of several factors, including o
 - [Deploy Azure OpenAI models with Azure AI Studio](../how-to/deploy-models-openai.md)
 - [Deploy Meta Llama 3.1 models with Azure AI Studio](../how-to/deploy-models-llama.md)
 - [Deploy large language models with Azure AI Studio](../how-to/deploy-models-open.md)
-- [Azure AI Studio FAQ](../faq.yml)
+- [Model catalog and collections in Azure AI Studio](../how-to/model-catalog-overview.md)

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

@@ -92,7 +92,7 @@ where $H$ is the forecast horizon, $l_{\text{max}}$ is the maximum lag order, an
 
  $T_{\text{CV}} = 2H + (n_{\text{CV}} - 1) n_{\text{step}} + \text{max}(l_{\text{max}}, s_{\text{window}}) + 1$,
 
-where $n_{\text{CV}}$ is the number of cross-validation folds and $n_{\text{step}}$ is the CV step size, or offset between CV folds. The basic logic behind these formulas is that you should always have at least a horizon of training observations for each time series, including some padding for lags and cross-validation splits. See [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection) for more details on cross-validation for forecasting.
+where $n_{\text{CV}}$ is the number of cross-validation folds and $n_{\text{step}}$ is the CV step size, or offset between CV folds. The basic logic behind these formulas is that you should always have at least a horizon of training observations for each time series, including some padding for lags and cross-validation splits. See [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection-in-automl) for more details on cross-validation for forecasting.
 
 ### Missing data handling
 AutoML's time series models require regularly spaced observations in time. Regularly spaced, here, includes cases like monthly or yearly observations where the number of days between observations may vary. Prior to modeling, AutoML must ensure there are no missing series values _and_ that the observations are regular. Hence, there are two missing data cases:

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

@@ -10,7 +10,7 @@ ms.service: azure-machine-learning
 ms.subservice: automl
 ms.topic: concept-article
 ms.custom: automl, sdkv1
-ms.date: 09/25/2024
+ms.date: 10/01/2024
 
 #customer intent: As a developer, I want to use AutoML in Azure Machine Learning, so I can search for (sweep) and select forecasting models.
 ---
@@ -19,8 +19,6 @@ ms.date: 09/25/2024
 
 This article describes how automated machine learning (AutoML) in Azure Machine Learning searches for and selects forecasting models. If you're interested in learning more about the forecasting methodology in AutoML, see [Overview of forecasting methods in AutoML](concept-automl-forecasting-methods.md). To explore training examples for forecasting models in AutoML, see [Set up AutoML to train a time-series forecasting model with the SDK and CLI](how-to-auto-train-forecast.md).
 
-<a name="model-sweeping"></a>
-
 ## Model sweeping in AutoML
 
 The central task for AutoML is to train and evaluate several models and choose the best one with respect to the given primary metric. The word "model" in this case refers to both the model class, such as ARIMA or Random Forest, and the specific hyper-parameter settings that distinguish models within a class. For instance, ARIMA refers to a class of models that share a mathematical template and a set of statistical assumptions. Training, or _fitting_, an ARIMA model requires a list of positive integers that specify the precise mathematical form of the model. These values are the hyper-parameters. The models ARIMA(1, 0, 1) and ARIMA(2, 1, 2) have the same class, but different hyper-parameters. These definitions can be separately fit with the training data and evaluated against each other. AutoML searches, or _sweeps_, over different model classes and within classes by varying the hyper-parameters.
@@ -41,8 +39,6 @@ For a description of the different model types, see the [Forecasting models in A
 
 The amount of sweeping by AutoML depends on the forecasting job configuration. You can specify the stopping criteria as a time limit or a limit on the number of trials, or the equivalent number of models. Early termination logic can be used in both cases to stop sweeping if the primary metric isn't improving.
 
-<a name="model-selection"></a>
-
 ## Model selection in AutoML
 
 AutoML follows a three-phase process to search for and select forecasting models:

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -10,7 +10,7 @@ ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: balapv
-ms.date: 08/21/2024
+ms.date: 09/30/2024
 #Customer intent: As a data scientist, I want to understand the big picture about how Azure Machine Learning works.
 ---
 
@@ -41,7 +41,7 @@ This document provides a quick overview of these resources and assets.
 To use the Python SDK code examples in this article:
 
 1. Install the [Python SDK v2](https://aka.ms/sdk-v2-install)
-2. Create a connection to your Azure Machine Learning subscription.  The examples all rely on `ml_client`. To create a workspace, the connection does not need a workspace name, since you may not yet have one.  All other examples in this article require that the workspace name is included in the connection.
+2. Create a connection to your Azure Machine Learning subscription. The examples all rely on `ml_client`. To create a workspace, the connection doesn't need a workspace name, since you may not yet have one. All other examples in this article require that the workspace name is included in the connection.
 
     ```python
     # import required libraries
@@ -82,7 +82,7 @@ Sign in to [Azure Machine Learning studio](https://ml.azure.com).
 
 ## Workspace
 
-The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components and data asset.
+The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components, and data asset.
 
 ### Create a workspace
 
@@ -133,7 +133,7 @@ A compute is a designated compute resource where you run your job or host your e
 
 * **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.
 * **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
-* **Serverless compute** - a compute cluster you access on the fly.  When you use serverless compute, you don't need to create your own cluster. All compute lifecycle management is offloaded to Azure Machine Learning.
+* **Serverless compute** - a compute cluster you access on the fly. When you use serverless compute, you don't need to create your own cluster. All compute lifecycle management is offloaded to Azure Machine Learning.
 * **Inference 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.
 
@@ -171,11 +171,10 @@ az ml compute create --file my_compute.yml
 ```
 
 For the content of the file, see [compute YAML examples](https://github.com/Azure/azureml-examples/tree/main/cli/resources/compute).
-.
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Compute**.
 1. On the top, select a tab to specify the type of compute you want to create.
 1. Select **New** to create the new compute.
@@ -235,7 +234,7 @@ For the content of the file, see [datastore YAML examples](https://github.com/Az
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Data**.
 1. On the top, select **Datastores**.
 1. Select **Create** to create a new datastore.
@@ -246,7 +245,7 @@ To learn more about using a datastore, see [Create and manage data assets](how-t
 
 ## 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 Machine Learning supports three types of storage format for models:
+Azure Machine Learning models consist of one or more binary files 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 is 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`
@@ -260,13 +259,13 @@ For more information on how to create models in the registry, see [Work with mod
 
 ## Environment
 
-Azure Machine Learning environments are an encapsulation of the environment where your machine learning task happens. They specify the software packages, environment variables, and software settings around your training and scoring scripts. The environments are managed and versioned entities within your Machine Learning workspace. Environments enable reproducible, auditable, and portable machine learning workflows across a variety of computes.
+Azure Machine Learning environments are an encapsulation of the environment where your machine learning task happens. They specify the software packages, environment variables, and software settings around your training and scoring scripts. The environments are managed and versioned entities within your Machine Learning workspace. Environments enable reproducible, auditable, and portable machine learning workflows across various computes.
 
 ### Types of environment
 
 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).
+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 precreated 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 and installing packages or any other dependencies that your training or scoring script needs on the compute. Azure Machine Learning allows you to create your own environment using
 
@@ -291,7 +290,7 @@ For more information, see [environment YAML schema](reference-yaml-environment.m
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Environments**.
 1. On the top, select **Custom environments**.
 1. Select **Create** to create a new custom environment.
@@ -314,7 +313,7 @@ Azure Machine Learning allows you to work with different types of data:
   * `boolean`
   * `number`
 
-For most scenarios, you'll use URIs (`uri_folder` and `uri_file`) - a location in storage that can be easily mapped to the filesystem of a compute node in a job by either mounting or downloading the storage to the node.
+For most scenarios, you use URIs (`uri_folder` and `uri_file`) - a location in storage that can be easily mapped to the filesystem of a compute node in a job by either mounting or downloading the storage to the node.
 
 `mltable` is an abstraction for tabular data that is to be used for AutoML Jobs, Parallel Jobs, and some advanced scenarios. If you're just starting to use Azure Machine Learning and aren't using AutoML, we strongly encourage you to begin with URIs.
 

articles/machine-learning/concept-component.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.author: lagayhar
 author: lgayhardt
 ms.reviewer: keli19
-ms.date: 01/19/2024
+ms.date: 09/30/2024
 ms.topic: conceptual
 ---
 # What is an Azure Machine Learning component?

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

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: lagayhar
 author: lgayhardt
 ms.reviewer: mesameki
-ms.date: 02/21/2024
+ms.date: 09/30/2024
 ms.custom: responsible-ml, devx-track-python
 #Customer intent: As a data scientist, I want to learn about machine learning fairness and how to assess and mitigate unfairness in machine learning models.
 ---