Merge pull request #638 from GitHubber17/300645-c

Freshness - Machine Learning how-to and concepts

articles/machine-learning/concept-automl-forecasting-at-scale.md

@@ -28,7 +28,7 @@ The many models [components](concept-component.md) in AutoML enable you to train
 
 The many models training component applies AutoML's [model sweeping and selection](concept-automl-forecasting-sweeping.md) independently to each store in this example. This model independence aids scalability and can benefit model accuracy especially when the stores have diverging sales dynamics. However, a single model approach may yield more accurate forecasts when there are common sales dynamics. See the [distributed DNN training](#distributed-dnn-training-preview) section for more details on that case.
 
-You can configure the data partitioning, the [AutoML settings](how-to-auto-train-forecast.md#configure-experiment) for the models, and the degree of parallelism for many models training jobs. For examples, see our guide section on [many models components](how-to-auto-train-forecast.md#forecasting-at-scale-many-models).        
+You can configure the data partitioning, the [AutoML settings](how-to-auto-train-forecast.md#configure-experiment) for the models, and the degree of parallelism for many models training jobs. For examples, see our guide section on [many models components](how-to-auto-train-forecast.md#forecast-at-scale-many-models).        
 
 ## Hierarchical time series forecasting
 
@@ -49,7 +49,7 @@ AutoML supports the following features for hierarchical time series (HTS):
 * **Retrieving quantile/probabilistic forecasts for levels at or "below" the training level**. Current modeling capabilities support disaggregation of probabilistic forecasts.
 
 HTS components in AutoML are built on top of [many models](#many-models), so HTS shares the scalable properties of many models. 
-For examples, see our guide section on [HTS components](how-to-auto-train-forecast.md#forecasting-at-scale-hierarchical-time-series).
+For examples, see our guide section on [HTS components](how-to-auto-train-forecast.md#forecast-at-scale-hierarchical-time-series).
 
 ## Distributed DNN training (preview)
 

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

@@ -78,7 +78,7 @@ In the table, $n_{\text{input}} = n_{\text{features}} + 1$, the number of predic
 
 ## TCNForecaster in AutoML
 
-TCNForecaster is an optional model in AutoML. To learn how to use it, see [enable deep learning](./how-to-auto-train-forecast.md#enable-deep-learning).
+TCNForecaster is an optional model in AutoML. To learn how to use it, see [enable deep learning](./how-to-auto-train-forecast.md#enable-learning-for-deep-neural-networks).
 
 In this section, we describe how AutoML builds TCNForecaster models with your data, including explanations of data preprocessing, training, and model search. 
 
@@ -90,9 +90,9 @@ AutoML executes several preprocessing steps on your data to prepare for model tr
 |--|--|
 Fill missing data|[Impute missing values and observation gaps](./concept-automl-forecasting-methods.md#missing-data-handling) and optionally [pad or drop short time series](./how-to-auto-train-forecast.md#short-series-handling)|
 |Create calendar features|Augment the input data with [features derived from the calendar](./concept-automl-forecasting-calendar-features.md) like day of the week and, optionally, holidays for a specific country/region.|
-|Encode categorical data|[Label encode](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html) strings and other categorical types; this includes all [time series ID columns](./how-to-auto-train-forecast.md#forecasting-job-settings).|
+|Encode categorical data|[Label encode](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html) strings and other categorical types; this includes all [time series ID columns](./how-to-auto-train-forecast.md#forecast-job-settings).|
 |Target transform|Optionally apply the natural logarithm function to the target depending on the results of certain statistical tests.|
-|Normalization|[Z-score normalize](https://en.wikipedia.org/wiki/Standard_score) all numeric data; normalization is performed per feature and per time series group, as defined by the [time series ID columns](./how-to-auto-train-forecast.md#forecasting-job-settings).
+|Normalization|[Z-score normalize](https://en.wikipedia.org/wiki/Standard_score) all numeric data; normalization is performed per feature and per time series group, as defined by the [time series ID columns](./how-to-auto-train-forecast.md#forecast-job-settings).
 
 These steps are included in AutoML's transform pipelines, so they're automatically applied when needed at inference time. In some cases, the inverse operation to a step is included in the inference pipeline. For example, if AutoML applied a $\log$ transform to the target during training, the raw forecasts are exponentiated in the inference pipeline.  
 
@@ -104,7 +104,7 @@ The following table lists and describes input settings and parameters for TCNFor
 
 |Training input|Description|Value|
 |--|--|--|
-|Validation data|A portion of data that is held out from training to guide the network optimization and mitigate over fitting.| [Provided by the user](./how-to-auto-train-forecast.md#training-and-validation-data) or automatically created from training data if not provided.|
+|Validation data|A portion of data that is held out from training to guide the network optimization and mitigate over fitting.| [Provided by the user](./how-to-auto-train-forecast.md#prepare-training-and-validation-data) or automatically created from training data if not provided.|
 |Primary metric|Metric computed from median-value forecasts on the validation data at the end of each training epoch; used for early stopping and model selection.|[Chosen by the user](./how-to-auto-train-forecast.md#configure-experiment); normalized root mean squared error or normalized mean absolute error.|
 |Training epochs|Maximum number of epochs to run for network weight optimization.|100; automated early stopping logic may terminate training at a smaller number of epochs. 
 |Early stopping patience|Number of epochs to wait for primary metric improvement before training is stopped.|20|

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

@@ -19,7 +19,7 @@ show_latex: true
 
 This article introduces concepts related to model inference and evaluation in forecasting tasks. For instructions and examples for training forecasting models in AutoML, see [Set up AutoML to train a time-series forecasting model with SDK and CLI](./how-to-auto-train-forecast.md).
 
-After you use AutoML to train and select a best model, the next step is to generate forecasts. Then, if possible, evaluate their accuracy on a test set held out from the training data. To see how to setup and run forecasting model evaluation in automated machine learning, see [Orchestrating training, inference, and evaluation](how-to-auto-train-forecast.md#orchestrating-training-inference-and-evaluation-with-components-and-pipelines).
+After you use AutoML to train and select a best model, the next step is to generate forecasts. Then, if possible, evaluate their accuracy on a test set held out from the training data. To see how to setup and run forecasting model evaluation in automated machine learning, see [Orchestrating training, inference, and evaluation](how-to-auto-train-forecast.md#orchestrate-training-inference-and-evaluation-with-components-and-pipelines).
 
 ## Inference scenarios
 
@@ -58,7 +58,7 @@ Suppose that after you train a model, you want to use it to make predictions fro
 
 :::image type="content" source="media/concept-automl-forecasting-evaluation/forecasting-with-gap-diagram.png" alt-text="Diagram demonstrating a forecast with a gap between the training and inference periods.":::
 
-AutoML supports this inference scenario, but you need to provide the context data in the gap period, as shown in the diagram. The prediction data passed to the [inference component](how-to-auto-train-forecast.md#orchestrating-training-inference-and-evaluation-with-components-and-pipelines) needs values for features and observed target values in the gap and missing values or `NaN` values for the target in the inference period. The following table shows an example of this pattern:  
+AutoML supports this inference scenario, but you need to provide the context data in the gap period, as shown in the diagram. The prediction data passed to the [inference component](how-to-auto-train-forecast.md#orchestrate-training-inference-and-evaluation-with-components-and-pipelines) needs values for features and observed target values in the gap and missing values or `NaN` values for the target in the inference period. The following table shows an example of this pattern:  
 
 :::image type="content" source="media/concept-automl-forecasting-evaluation/forecasting-with-gap-table.png" alt-text="Table showing an example of prediction data when there's a gap between the training and inference periods.":::
 
@@ -86,7 +86,7 @@ The context advances along with the forecasting window. Actual values from the t
 
 :::image type="content" source="media/concept-automl-forecasting-evaluation/rolling-evaluation-table.png" alt-text="Diagram shows example output table from a rolling forecast.":::
 
-With a table like this, you can visualize the forecasts versus the actuals and compute desired evaluation metrics. AutoML pipelines can generate rolling forecasts on a test set with an [inference component](how-to-auto-train-forecast.md#orchestrating-training-inference-and-evaluation-with-components-and-pipelines).
+With a table like this, you can visualize the forecasts versus the actuals and compute desired evaluation metrics. AutoML pipelines can generate rolling forecasts on a test set with an [inference component](how-to-auto-train-forecast.md#orchestrate-training-inference-and-evaluation-with-components-and-pipelines).
 
 > [!NOTE]
 > When the test period is the same length as the forecast horizon, a rolling forecast gives a single window of forecasts up to the horizon.