Merge pull request #582 from MicrosoftDocs/main

Publish to live, Tuesday 4 AM PST, 10/1

Author: ttorble

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-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.
 ---