ML.NET doc updates

docs/machine-learning/automate-training-with-cli.md

@@ -1,7 +1,7 @@
 ---
 title: Automate model training with the ML.NET CLI
 description: Discover how to use the ML.NET CLI tool to automatically train the best model from the command-line.
-ms.date: 06/03/2020
+ms.date: 11/10/2022
 ms.custom: how-to, mlnet-tooling
 #Customer intent: As a developer, I want to use ML.NET CLI to automatically train the "best model" from the command-prompt. I also want to understand the output provided by the tool (metrics and output assets)
 ---
@@ -71,7 +71,7 @@ The following image displays the classification metrics list for the top five mo
 
  Accuracy is a popular metric for classification problems, however accuracy isn't always the best metric to select the best model from as explained in the following references. There are cases where you need to evaluate the quality of your model with additional metrics.
 
-To explore and understand the metrics that are output by the CLI, see [Evaluation metrics for classification](resources/metrics.md#evaluation-metrics-for-multi-class-classification).
+To explore and understand the metrics that are output by the CLI, see [Evaluation metrics for classification](resources/metrics.md#evaluation-metrics-for-multi-class-classification-and-text-classification).
 
 ### Metrics for Regression and Recommendation models
 

docs/machine-learning/automate-training-with-model-builder.md

@@ -1,7 +1,7 @@
 ---
 title: What is Model Builder and how does it work?
 description: How to use the ML.NET Model Builder to automatically train a machine learning model
-ms.date: 10/12/2021
+ms.date: 11/10/2022
 ms.custom: overview, mlnet-tooling
 #Customer intent: As a developer, I want to use Model Builder to automatically train a model using a visual interface.
 ---
@@ -13,10 +13,7 @@ Model Builder uses automated machine learning (AutoML) to explore different mach
 
 You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application.
 
-![Model Builder Scenarios](./media/model-builder-scenarios.png#lightbox)
-
-> [!NOTE]
-> Model Builder is currently in Preview.
+:::image type="content" source="media/model-builder-scenarios-2-0.png" alt-text="Model Builder scenario screen" lightbox="media/model-builder-scenarios-2-0.png":::
 
 ## Creating a Model Builder Project
 
@@ -45,14 +42,15 @@ A scenario is a description of the type of prediction you want to make using you
 
 Each scenario maps to a different Machine Learning Task which include:
 
-- Binary classification
-- Multiclass classification
-- Regression
-- Clustering
-- Anomaly detection
-- Ranking
-- Recommendation
-- Forecasting
+| Task | Scenario |
+| --- | --- |
+| Binary classification | Data classification |
+| Multiclass classification | Data classification |
+| Image classification | Image classification |
+| Text classification | Text classification |
+| Regression | Value prediction |
+| Recommendation | Recommendation |
+| Forecasting | Forecasting |
 
 For example, the scenario of classifying sentiments as positive or negative would fall under the binary classification task.
 
@@ -62,49 +60,212 @@ For more information about the different ML Tasks supported by ML.NET see [Machi
 
 In Model Builder, you need to select a scenario. The type of scenario depends on what type of prediction you are trying to make.
 
-#### Data classification
-
-Classification is used to categorize data into categories.
+#### Tabular
 
-![Diagram showing examples of binary classification including fraud detection, risk mitigation, and application screening](media/binary-classification-examples.png)
+##### Data classification
 
-![Examples of multiclass classification including document and product classification, support ticket routing, and customer issue prioritization](media/multiclass-classification-examples.png)
+Classification is used to categorize data into categories.
 
-#### Value prediction
+<!-- ![Diagram showing examples of binary classification including fraud detection, risk mitigation, and application screening](media/binary-classification-examples.png)
+
+![Examples of multiclass classification including document and product classification, support ticket routing, and customer issue prioritization](media/multiclass-classification-examples.png) -->
+
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        | SepalLength | SepalWidth | Petal Length | Petal Width | Species |
+        | --- | --- | --- | --- | --- |
+        | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
+    :::column-end:::
+    :::column:::
+        | Predicted species |
+        | --- |
+        | setosa |
+    :::column-end:::
+:::row-end:::
+
+##### Value prediction
 
 Value prediction, which falls under the regression task, is used to predict numbers.
 
 ![Diagram showing regression examples such as price prediction, sales forecasting, and predictive maintenance](media/regression-examples.png)
 
-#### Image classification
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        | vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance | payment_type | fare_amount |
+        | --- | --- | --- | --- | --- | --- | --- |
+        | CMT | 1 | 1 | 1271 | 3.8 | CRD | 17.5 |
+    :::column-end:::
+    :::column:::
+        | Predicted Fare |
+        | --- |
+        | 4.5 |
+    :::column-end:::
+:::row-end:::
+
+#### Recommendation
+
+The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'.
+
+You can use the recommendation scenario when you have a set of users and a set of "products", such as items to purchase, movies, books, or TV shows, along with a set of users' "ratings" of those products.
+
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        | UserId | ProductId | Rating |
+        | --- | --- | --- |
+        | 1 | 2 | 4.2 |
+    :::column-end:::
+    :::column:::
+        | Predicted rating |
+        | --- |
+        | 4.5 |
+    :::column-end:::
+:::row-end:::
+
+##### Forecasting
+
+The forecasting scenario uses historical data with a time-series or seasonal component to it.
+
+You can use the forecasting scenario to forecast demand or sale for a product.
+
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        | Date | SaleQty |
+        | --- | --- |
+        | 1/1/1970 | 1000 |
+    :::column-end:::
+    :::column:::
+        | 3 Day Forecast |
+        | --- |
+        | [1000,1001,1002] |
+    :::column-end:::
+:::row-end:::
+
+#### Computer Vision
+
+##### Image classification
 
 Image classification is used to identify images of different categories. For example, different types of terrain or animals or manufacturing defects.
 
 You can use the image classification scenario if you have a set of images, and you want to classify the images into different categories.
 
-#### Object detection
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        :::image type="content" source="media/automate-training-with-model-builder/dog-classification.png" alt-text="Profile view of standing pug":::
+    :::column-end:::
+    :::column:::
+        | Predicted Label |
+        | --- |
+        | Dog |
+    :::column-end:::
+:::row-end:::
+
+##### Object detection
 
 Object detection is used to locate and categorize entities within images.  For example, locating and identifying cars and people in an image.
 
 You can use object detection when images contain multiple objects of different types.
 
-#### Recommendation
-
-The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'.
-
-You can use the recommendation scenario when you have a set of users and a set of "products", such as items to purchase, movies, books, or TV shows, along with a set of users' "ratings" of those products.
+:::row:::
+    :::column:::
+        **Sample Input**
+    :::column-end:::
+    :::column:::
+        **Sample Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        :::image type="content" source="media/automate-training-with-model-builder/dog-classification.png" alt-text="Profile view of standing pug":::
+    :::column-end:::
+    :::column:::
+        :::image type="content" source="media/automate-training-with-model-builder/dog-object-detection-min.png" alt-text="Profile view of standing pug with bounding box and dog label":::
+    :::column-end:::
+:::row-end:::
+
+#### Natural Language Processing
+
+##### Text classification
+
+Text classification categorizes raw text input.
+
+You can use the text classification scenario if you have a set of documents or comments, and you want to classify them into different categories.
+
+:::row:::
+    :::column:::
+        **Example Input**
+    :::column-end:::
+    :::column:::
+        **Example Output**
+    :::column-end:::
+:::row-end:::
+:::row:::
+    :::column:::
+        | Review |
+        | ---  |
+        | I really like this steak!|
+    :::column-end:::
+    :::column:::
+        | Sentiment |
+        | --- |
+        | Positive |
+    :::column-end:::
+:::row-end:::
 
 ## Environment
 
 You can train your machine learning model locally on your machine or in the cloud on Azure, depending on the scenario.
 
 When you train locally, you work within the constraints of your computer resources (CPU, memory, and disk). When you train in the cloud, you can scale up your resources to meet the demands of your scenario, especially for large datasets.
 
-Local CPU training is supported for all scenarios except Object Detection.
-
-Local GPU training is supported for Image Classification.
-
-Azure training is supported for Image Classification and Object Detection.
+| Scenario             | Local CPU | Local GPU | Azure |
+|-----------------------|------------|------------|--------|
+| Data classification  | ✔️       | ❌         | ❌    |
+| Value prediction     | ✔️       | ❌         | ❌    |
+| Recommendation       | ✔️       | ❌         | ❌    |
+| Forecasting          | ✔️       | ❌         | ❌    |
+| Image classification | ✔️       | ✔️        | ✔️   |
+| Object detection     | ❌        | ❌         | ✔️   |
+| Text classification  | ✔️       | ✔️        | ❌    |
 
 ## Data
 

docs/machine-learning/automated-machine-learning-mlnet.md

@@ -0,0 +1,71 @@
+---
+title: What is Automated Machine Learning (AutoML)?
+description: Learn what automated machine learning is and its different components in ML.NETs
+ms.date: 11/10/2022
+ms.topic: overview
+ms.custom: mvc
+---
+
+# What is Automated Machine Learning (AutoML)?
+
+Automated machine learning (AutoML) automates the process of applying machine learning to data. Given a dataset, you can run AutoML to iterate over different data transformations, machine learning algorithms, and hyperparameters to select the best model.
+
+> [!NOTE]
+> This topic refers to the ML.NET AutoML API, which is currently in preview. Material may be subject to change.
+
+## How does AutoML work?
+
+In general, the workflow to train machine learning models is as follows:
+
+- Define a problem
+- Collect data
+- Preprocess data
+- Train a model
+- Evaluate the model
+
+:::image type="content" source="media/ml-automl-workflow.png" alt-text="Traditional ML and AutoML training workflow" lightbox="media/ml-automl-workflow.png":::
+
+Preprocessing, training, and evaluation are an experimental and iterative process that requires multiple trials until you achieve satisfactory results. Because these tasks tend to be repetitive, AutoML can help automate these steps. In addition to automation, optimization techniques are used during the training and evaluation process to find and select algorithms and hyperparameters.
+
+## When should I use AutoML?
+
+Whether you're just getting started with machine learning or you're an experienced user, AutoML provides solutions for automating the model development process.
+
+- **Beginners** - If you're new to machine learning, AutoML simplifies the model development process by providing a set of defaults that reduces the number of decisions you have to make when training your model. In doing so, you can focus on your data and the problem you're trying to solve and let AutoML do the rest.  
+- **Experienced users** - If you have some experience with machine learning, you can customize, configure, and extend the defaults provided by AutoML based on your needs while still leveraging its automation capabilities.  
+
+## AutoML in ML.NET
+
+- **Featurizer** - Convenience API to automate data preprocessing.
+- **Trial** - A single hyperparamters optimization run.
+- **Experiment** - A collection of AutoML trials. ML.NET provides a high-level API for creating experiments which sets defaults for the individual Sweepable Pipeline, Search Space, and Tuner components.
+- **Search Space** - The range of available options to choose hyperparameters from.
+- **Tuner** - The algorithms used to optimize hyperparameters. ML.NET supports the following tuners:
+  - **Cost Frugal Tuner** - Implementation of [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571) which takes training cost into consideration
+  - **Eci Cost Frugal Tuner** - Implementation of Cost Frugal Tuner for hierarchical search spaces. Default tuner used by AutoML.
+  - **SMAC** - Tuner that uses random forests to apply Bayesian optimization.
+  - **Grid Search** - Tuner that works best for small search spaces.
+  - **Random Search**
+- **Sweepable Estimator** - An ML.NET estimator that contains a search space.
+- **Sweepable Pipeline** - An ML.NET pipeline that contains one or more Sweepable Estimators.
+- **Trial Runner** - AutoML component that uses sweepable pipelines and trial settings to generate trial results from model training and evaluation.  
+
+It's recommended for beginners to start with the defaults provided by the high-level experiment API. For more experienced users looking for customization options, use the sweepable estimator, sweepable pipeline, search space, trial runner and tuner components.
+
+For more information on getting started with the AutoML API, see the [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) guide.
+
+## Supported tasks
+
+AutoML provides preconfigured defaults for the following tasks:
+
+- Binary classification
+- Multiclass classification
+- Regression
+
+For other tasks, you can build your own trial runner to enable those scenarios. For more information, see the [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) guide.  
+
+## Next steps
+
+- [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md)
+- [Tutorial: Classify the severity of restaurant health violations with Model Builder](tutorials/health-violation-classification-model-builder.md)
+- [Tutorial: Analyze sentiment using the ML.NET CLI](tutorials/sentiment-analysis-cli.md)