Add release notes for ML.NET 0.8

docs/release-notes/0.8/release-0.8.md

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+# ML.NET 0.8 Release Notes
+
+Today we are excited to release ML.NET 0.8 and we can finally explain why it
+is the best version so far! This release enables model explainability to
+understand which features (inputs) are most important, improved debuggability,
+easier to use time series predictions, several API improvements, a new
+recommendation use case, and more.
+
+### Installation
+
+ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET
+Core
+2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md)
+for more details.
+
+You can install ML.NET NuGet from the CLI using:
+```
+dotnet add package Microsoft.ML
+```
+
+From package manager:
+```
+Install-Package Microsoft.ML
+```
+
+### Release Notes
+
+Below are some of the highlights from this release.
+
+* Added first steps towards model explainability
+  ([#1735](https://github.com/dotnet/machinelearning/pull/1735),
+  [#1692](https://github.com/dotnet/machinelearning/pull/1692))
+
+    * Enabled explainability in the form of overall feature importance and
+      generalized additive models. 
+    * Overall feature importance gives a sense of which features are overall
+      most important for the model. For example, when predicting the sentiment
+      of a tweet, the presence of "amazing" might be more important than
+      whether the tweet contains "bird". This is enabled through Permutation
+      Feature Importance. Example usage can be found
+      [here](https://github.com/dotnet/machinelearning/blob/3d33e20f33da70cdd3da2ad9e0b2b03df929bef4/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance.cs).
+    * Generalized Additive Models have very explainable predictions. They are
+      similar to linear models in terms of ease of understanding but are more
+      flexible and can have better performance. Example usage can be found
+      [here](https://github.com/dotnet/machinelearning/blob/3d33e20f33da70cdd3da2ad9e0b2b03df929bef4/docs/samples/Microsoft.ML.Samples/Dynamic/GeneralizedAdditiveModels.cs).
+
+* Improved debuggability by previewing IDataViews
+  ([#1518](https://github.com/dotnet/machinelearning/pull/1518))
+
+    * It is often useful to peek at the data that is read into an ML.NET
+      pipeline and even look at it after some intermediate steps to ensure the
+      data is transformed as expected. 
+    * You can now preview an IDataView by going to the Watch window in the VS
+      debugger, entering a variable name you want to preview and calling its
+      `Preview()` method. 
+
+    ![](dataPreview.gif)
+
+* Enabled a stateful prediction engine for time series problems
+  ([#1727](https://github.com/dotnet/machinelearning/pull/1727))
+
+    * [ML.NET
+      0.7](https://github.com/dotnet/machinelearning/blob/483ec04a11fbdc056a88bc581d7e5cee9092a936/docs/release-notes/0.7/release-0.7.md)
+      enabled anomaly detection scenarios. However, the prediction engine was
+      stateless, which means that every time you want to figure out whether
+      the latest data point is anomolous, you need to provide historical data
+      as well. This is unnatural.
+    * The prediction engine can now keep state of time series data seen so
+      far, so you can now get predictions by just providing the latest data
+      point. This is enabled by using `CreateTimeSeriesPredictionFunction`
+      instead of `MakePredictionFunction`. Example usage can be found
+      [here](https://github.com/dotnet/machinelearning/blob/3d33e20f33da70cdd3da2ad9e0b2b03df929bef4/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs#L141).
+      You'll need to add the Microsoft.ML.TimeSeries NuGet to your project.
+
+* Improved support for recommendation scenarios with implicit feedback
+  ([#1664](https://github.com/dotnet/machinelearning/pull/1664))  
+
+    * [ML.NET
+      0.7](https://github.com/dotnet/machinelearning/blob/483ec04a11fbdc056a88bc581d7e5cee9092a936/docs/release-notes/0.7/release-0.7.md)
+      included Matrix Factorization which enables using ratings provided by
+      users to recommend other items they might like.
+    * In some cases, you don't have specific ratings from users but only
+      implicit feedback (e.g. they watched the movie but didn't rate it).
+    * Matrix Factorization in ML.NET can now use this type of implicit data to
+      train models for recommendation scenarios. 
+    * Example usage can be found
+      [here](https://github.com/dotnet/machinelearning/blob/71d58fa83f77abb630d815e5cf8aa9dd3390aa65/test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs#L335).
+      You'll need to add the Microsoft.ML.MatrixFactorization NuGet to your
+      project.
+
+* Enabled saving and loading data as a binary file (IDataView/IDV)
+  ([#1678](https://github.com/dotnet/machinelearning/pull/1678))
+
+    * It is sometimes useful to save data after it has been transformed. For
+      example, you might have featurized all the text into sparse vectors and
+      want to perform repeated experimentation with different trainers without
+      continuously repeating the data transformation.
+    * Saving and loading files in ML.NET's binary format can help efficiency
+      as it is compressed and already schematized.
+    * Reading a binary data file can be done using
+      `mlContext.Data.ReadFromBinary("pathToFile")` and writing a binary data
+      file can be done using `mlContext.Data.SaveAsBinary("pathToFile")`.
+
+* Added filtering and caching APIs
+  ([#1569](https://github.com/dotnet/machinelearning/pull/1569))
+
+    * There is sometimes a need to filter the data used for training a model.
+      For example, you need to remove rows that don't have a label, or focus
+      your model on certain categories of inputs. This can now be done with
+      additional filters as shown
+      [here](https://github.com/dotnet/machinelearning/blob/71d58fa83f77abb630d815e5cf8aa9dd3390aa65/test/Microsoft.ML.Tests/RangeFilterTests.cs#L30).
+
+    * Some estimators iterate over the data multiple times. Instead of always
+      reading from file, you can choose to cache the data to potentially speed
+      things up. An example can be found
+      [here](https://github.com/dotnet/machinelearning/blob/71d58fa83f77abb630d815e5cf8aa9dd3390aa65/test/Microsoft.ML.Tests/CachingTests.cs#L56).
+
+### Acknowledgements
+
+Shoutout to [jwood803](https://github.com/jwood803),
+[feiyun0112](https://github.com/feiyun0112),
+[bojanmisic](https://github.com/bojanmisic),
+[rantri](https://github.com/rantri), [Caraul](https://github.com/Caraul),
+[van-tienhoang](https://github.com/van-tienhoang),
+[Thomas-S-B](https://github.com/Thomas-S-B), and the ML.NET team for their
+contributions as part of this release!