Update .NET documentation (#643)

* Updated install instructions

* Update website/docs/Installation.md

* Updated install and README

README.md

@@ -22,11 +22,12 @@ learners and hyperparameters for each learner.
 hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
 and learner selection method invented by Microsoft Research.
 
-FLAML has a .NET implementation as well from [ML.NET Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) in [Visual Studio](https://visualstudio.microsoft.com/) 2022. This [ML.NET blog](https://devblogs.microsoft.com/dotnet/ml-net-june-updates/#new-and-improved-automl) describes the improvement brought by FLAML.
-
+FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
 
 ## Installation
 
+### Python
+
 FLAML requires **Python version >= 3.7**. It can be installed from pip:
 
 ```bash
@@ -40,6 +41,14 @@ install flaml with the [notebook] option:
 pip install flaml[notebook]
 ```
 
+### .NET
+
+Use the following guides to get started with FLAML in .NET:
+
+- [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
+- [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
+- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0-preview.22313.1)
+
 ## Quickstart
 
 * With three lines of code, you can start using this economical and fast
@@ -89,6 +98,8 @@ In addition, you can find:
 
 - Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
 
+- ML.NET documentation and tutorials for [Model Builder](https://docs.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/03-Training%20and%20AutoML.ipynb).
+
 ## Contributing
 
 This project welcomes contributions and suggestions. Most contributions require you to agree to a

website/docs/Installation.md

@@ -1,5 +1,7 @@
 # Installation
 
+## Python
+
 FLAML requires **Python version >= 3.7**. It can be installed from pip:
 
 ```bash
@@ -11,11 +13,10 @@ or conda:
 conda install flaml -c conda-forge
 ```
 
-FLAML has a .NET implementation as well from [ML.NET Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) in [Visual Studio](https://visualstudio.microsoft.com/) 2022.
+### Optional Dependencies
 
-## Optional Dependencies
+#### Notebook
 
-### Notebook
 To run the [notebook examples](https://github.com/microsoft/FLAML/tree/main/notebook),
 install flaml with the [notebook] option:
 
@@ -23,7 +24,8 @@ install flaml with the [notebook] option:
 pip install flaml[notebook]
 ```
 
-### Extra learners
+#### Extra learners
+
 * catboost
 ```bash
 pip install flaml[catboost]
@@ -42,7 +44,8 @@ pip install flaml[forecast]
 pip install flaml[nlp]
 ```
 
-### Distributed tuning
+#### Distributed tuning
+
 * ray
 ```bash
 pip install flaml[ray]
@@ -56,7 +59,8 @@ pip install flaml[nni]
 pip install flaml[blendsearch]
 ```
 
-### Test and Benchmark
+#### Test and Benchmark
+
 * test
 ```bash
 pip install flaml[test]
@@ -66,3 +70,19 @@ pip install flaml[test]
 pip install flaml[benchmark]
 ```
 
+## .NET
+
+FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET.
+
+You can use FLAML in .NET in the following ways:
+
+**Low-code**
+
+- [*Model Builder*](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) - A Visual Studio extension for training ML models using FLAML. For more information on how to install the, see the [install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022) guide.
+- [*ML.NET CLI*](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli) - A dotnet CLI tool for training machine learning models using FLAML on Windows, MacOS, and Linux. For more information on how to install the ML.NET CLI, see the [install the ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows) guide. 
+
+**Code-first**
+
+- [*Microsoft.ML.AutoML*](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0-preview.22313.1) - NuGet package that provides direct access to the FLAML AutoML APIs that power low-code solutions like Model Builder and the ML.NET CLI. For more information on installing NuGet packages, see the install and use a NuGet package in [Visual Studio](https://docs.microsoft.com/nuget/quickstart/install-and-use-a-package-in-visual-studio) or [dotnet CLI](https://docs.microsoft.com/nuget/quickstart/install-and-use-a-package-using-the-dotnet-cli) guides. 
+
+To get started with the ML.NET API and AutoML, see the [csharp-notebooks](https://github.com/dotnet/csharp-notebooks#machine-learning).