Merge pull request #107040 from nibaccam/automl-refresh

Auto ML | freshness review

Author: PRMerger17

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

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.reviewer: trbye
 ms.topic: conceptual
-ms.date: 11/04/2019
+ms.date: 03/09/2020
 ---
 
 # Auto-train a time-series forecast model
@@ -23,7 +23,7 @@ In this article, you learn how to train a time-series forecasting regression mod
 
 > [!VIDEO https://www.microsoft.com/videoplayer/embed/RE2X1GW]
 
-You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast. An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are “pivoted” to become additional dimensions for the regressor together with other predictors.
+You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast. An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are "pivoted" to become additional dimensions for the regressor together with other predictors.
 
 This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. In real-world forecasting applications, multiple factors can influence a forecast. For example, when forecasting sales, interactions of historical trends, exchange rate and price all jointly drive the sales outcome. A further benefit is that all recent innovations in regression models apply immediately to forecasting.
 

articles/machine-learning/how-to-auto-train-remote.md

@@ -10,7 +10,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.workload: data-services
 ms.topic: conceptual
-ms.date: 11/04/2019
+ms.date: 03/09/2020
 
 #Customer intent: As a professional data scientist, I can use automated machine learning (automated ML) functionality to build a model on an Azure Machine Learning remote compute target.
 ---

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

@@ -9,7 +9,7 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 11/04/2019
+ms.date: 03/09/2020
 ms.custom: seodec18
 ---
 
@@ -125,8 +125,8 @@ Use custom validation dataset if random split is not acceptable, usually time se
 ## Compute to run experiment
 
 Next determine where the model will be trained. An automated machine learning training experiment can run on the following compute options:
-*	Your local machine such as a local desktop or laptop – Generally when you have small dataset and you are still in the exploration stage.
-*	A remote machine in the cloud – [Azure Machine Learning Managed Compute](concept-compute-target.md#amlcompute) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines.
+*    Your local machine such as a local desktop or laptop – Generally when you have small dataset and you are still in the exploration stage.
+*    A remote machine in the cloud – [Azure Machine Learning Managed Compute](concept-compute-target.md#amlcompute) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines.
 
     See this [GitHub site](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning) for examples of notebooks with local and remote compute targets.
 
@@ -142,7 +142,7 @@ There are several options that you can use to configure your automated machine l
 
 Some examples include:
 
-1.	Classification experiment using AUC weighted as the primary metric with experiment timeout minutes set to 30 minutes and 2 cross-validation folds.
+1.    Classification experiment using AUC weighted as the primary metric with experiment timeout minutes set to 30 minutes and 2 cross-validation folds.
 
     ```python
     automl_classifier=AutoMLConfig(
@@ -154,7 +154,7 @@ Some examples include:
         label_column_name=label,
         n_cross_validations=2)
     ```
-2.	Below is an example of a regression experiment set to end after 60 minutes with five validation cross folds.
+2.    Below is an example of a regression experiment set to end after 60 minutes with five validation cross folds.
 
     ```python
     automl_regressor = AutoMLConfig(
@@ -367,7 +367,7 @@ Use these 2 APIs on the first step of fitted model to understand more.  See [thi
   This list includes all engineered feature names.
 
   >[!Note]
-  >Use 'timeseriestransformer' for task=’forecasting’, else use 'datatransformer' for ‘regression’ or ‘classification’ task.
+  >Use 'timeseriestransformer' for task='forecasting', else use 'datatransformer' for 'regression' or 'classification' task.
 
 + API 2: `get_featurization_summary()` returns featurization summary for all the input features.
 
@@ -377,7 +377,7 @@ Use these 2 APIs on the first step of fitted model to understand more.  See [thi
   ```
 
   >[!Note]
-  >Use 'timeseriestransformer' for task=’forecasting’, else use 'datatransformer' for ‘regression’ or ‘classification’ task.
+  >Use 'timeseriestransformer' for task='forecasting', else use 'datatransformer' for 'regression' or 'classification' task.
 
   Output:
   ```

articles/machine-learning/how-to-create-portal-experiments.md

@@ -1,7 +1,7 @@
 ---
-title: Build & deploy automated ML models
+title: Use autoML to create models & deploy 
 titleSuffix: Azure Machine Learning
-description: Create, manage, and deploy automated machine learning experiments in Azure Machine Learning studio.
+description: Create, review, and deploy automated machine learning models with Azure Machine Learning.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -10,26 +10,28 @@ ms.author: nibaccam
 author: tsikiksr
 manager: cgronlun
 ms.reviewer: nibaccam
-ms.date: 02/04/2020
+ms.date: 03/10/2020
 
 ---
 
-# Create, explore, and deploy automated machine learning experiments with Azure Machine Learning studio
+# Create, review, and deploy automated machine learning models with Azure Machine Learning
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-enterprise-sku.md)]
 
- In this article, you learn how to create, explore, and deploy automated machine learning experiments in Azure Machine Learning studio without a single line of code. Automated machine learning automates the process of selecting the best algorithm to use for your specific data, so you can generate a machine learning model quickly. [Learn more about automated machine learning](concept-automated-ml.md).
+In this article, you learn how to create, explore, and deploy automated machine learning models without a single line of code in Azure Machine Learning's studio interface. Automated machine learning is a process in which the best machine learning algorithm to use for your specific data is selected for you. This process enables you to generate machine learning models quickly. [Learn more about automated machine learning](concept-automated-ml.md).
+ 
+For an end to end example, try the [tutorial for creating a classification model with Azure Machine Learning's automated ML interface](tutorial-first-experiment-automated-ml.md). 
 
- If you prefer a more code-based experience, you can also [configure your automated machine learning experiments in Python](how-to-configure-auto-train.md) with the [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py).
+For a Python code-based experience, [configure your automated machine learning experiments](how-to-configure-auto-train.md) with the Azure Machine Learning SDK.
 
 ## Prerequisites
 
-* An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
+* An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
 * An Azure Machine Learning workspace with a type of **Enterprise edition**. See [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).  To upgrade an existing workspace to Enterprise edition, see [Upgrade to Enterprise edition](how-to-manage-workspace.md#upgrade).
 
 ## Get started
 
-1. Sign in to [Azure Machine Learning studio](https://ml.azure.com). 
+1. Sign in to Azure Machine Learning at https://ml.azure.com. 
 
 1. Select your subscription and workspace. 
 
@@ -164,7 +166,7 @@ Automated machine learning offers preprocessing and data guardrails automaticall
 |Impute missing values|For numerical features, impute with average of values in the column.<br/><br/>For categorical features, impute with most frequent value.|
 |Generate additional features|For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.<br/><br/>For Text features: Term frequency based on unigrams, bi-grams, and tri-character-grams.|
 |Transform and encode |Numeric features with few unique values are transformed into categorical features.<br/><br/>One-hot encoding is performed for low cardinality categorical; for high cardinality, one-hot-hash encoding.|
-|Word embeddings|Text featurizer that converts vectors of text tokens into sentence vectors using a pre-trained model. Each word’s embedding vector in a document is aggregated together to produce a document feature vector.|
+|Word embeddings|Text featurizer that converts vectors of text tokens into sentence vectors using a pre-trained model. Each word's embedding vector in a document is aggregated together to produce a document feature vector.|
 |Target encodings|For categorical features, maps each category with averaged target value for regression problems, and to the class probability for each class for classification problems. Frequency-based weighting and k-fold cross validation is applied to reduce over fitting of the mapping and noise caused by sparse data categories.|
 |Text target encoding|For text input, a stacked linear model with bag-of-words is used to generate the probability of each class.|
 |Weight of Evidence (WoE)|Calculates WoE as a measure of correlation of categorical columns to the target column. It is calculated as the log of the ratio of in-class vs out-of-class probabilities. This step outputs one numerical feature column per class and removes the need to explicitly impute missing values and outlier treatment.|
@@ -178,10 +180,10 @@ The following table describes the currently supported data guardrails, and the a
 
 Guardrail|Status|Condition&nbsp;for&nbsp;trigger
 ---|---|---
-Missing&nbsp;values&nbsp;imputation |**Passed** <br> <br> **Fixed**|	No missing value in any of the input&nbsp;columns <br> <br> Some columns have missing values
+Missing&nbsp;values&nbsp;imputation |**Passed** <br> <br> **Fixed**|    No missing value in any of the input&nbsp;columns <br> <br> Some columns have missing values
 Cross validation|**Done**|If no explicit validation set is provided
-High&nbsp;cardinality&nbsp;feature&nbsp;detection|	**Passed** <br> <br>**Done**|	No high cardinality features were detected <br><br> High cardinality input columns were detected
-Class balance detection	|**Passed** <br><br><br>**Alerted** |Classes are balanced in the training data; A dataset is considered balanced if each class has good representation in the dataset, as measured by number and ratio of samples <br> <br> Classes in the training data are imbalanced
+High&nbsp;cardinality&nbsp;feature&nbsp;detection|    **Passed** <br> <br>**Done**|    No high cardinality features were detected <br><br> High cardinality input columns were detected
+Class balance detection    |**Passed** <br><br><br>**Alerted** |Classes are balanced in the training data; A dataset is considered balanced if each class has good representation in the dataset, as measured by number and ratio of samples <br> <br> Classes in the training data are imbalanced
 Time-series data consistency|**Passed** <br><br><br><br> **Fixed** |<br> The selected {horizon, lag, rolling window} value(s) were analyzed, and no potential out-of-memory issues were detected. <br> <br>The selected {horizon, lag, rolling window} values were analyzed and will potentially cause your experiment to run out of memory. The lag or rolling window has been turned off.
 
 ## Run experiment and view results
@@ -232,11 +234,10 @@ Automated ML helps you with deploying the model without writing code:
 
 1. Select **Deploy**. Deployment can take about 20 minutes to complete.
 
-Now you have an operational web service to generate predictions! You can test the predictions by querying the service from [Power BI’s built in Azure Machine Learning support](how-to-consume-web-service.md#consume-the-service-from-power-bi).
+Now you have an operational web service to generate predictions! You can test the predictions by querying the service from [Power BI's built in Azure Machine Learning support](how-to-consume-web-service.md#consume-the-service-from-power-bi).
 
 ## Next steps
 
-* Try the end to end [tutorial for creating your first automated ML experiment with Azure Machine Learning studio](tutorial-first-experiment-automated-ml.md). 
-* [Learn more about automated machine learning](concept-automated-ml.md) and Azure Machine Learning.
-* [Understand automated machine learning results](how-to-understand-automated-ml.md).
 * [Learn how to consume a web service](https://docs.microsoft.com/azure/machine-learning/how-to-consume-web-service).
+* [Understand automated machine learning results](how-to-understand-automated-ml.md).
+* [Learn more about automated machine learning](concept-automated-ml.md) and Azure Machine Learning.

articles/machine-learning/how-to-train-ml-models.md

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
 ms.reviewer: sgilley
-ms.date: 11/08/2019
+ms.date: 03/09/2020
 ms.custom: seodec18
 
 ---
@@ -34,7 +34,7 @@ This article focuses on steps 4-5. For steps 1-3, refer to the [train a model tu
 
 ### Single-node training
 
-Use an `Estimator` for a single-node training run on remote compute in Azure for a scikit-learn model. You should have already created your [compute target](how-to-set-up-training-targets.md#amlcompute) object `compute_target` and your [datastore](how-to-access-data.md) object `ds`.
+Use an `Estimator` for a single-node training run on remote compute in Azure for a scikit-learn model. You should have already created your [compute target](how-to-set-up-training-targets.md#amlcompute) object `compute_target` and your [FileDataset](how-to-create-register-datasets.md) object `ds`.
 
 ```Python
 from azureml.train.estimator import Estimator