Windows 10: Announcing ML.NET 0.8 - Machine Learning for .NET

Brink · Dec 4, 2018

ML.NET is an open-source and cross-platform framework (Windows, Linux, macOS) which makes machine learning accessible for .NET developers.

ML.NET allows you to create and use machine learning models targeting scenarios to achieve common tasks such as sentiment analysis, issue classification, forecasting, recommendations, fraud detection, image classification and more. You can check out these common tasks at our GitHub repo with ML.NET samples.

Today we’re happy to announce the release of ML.NET 0.8. (ML.NET 0.1 was released at //Build 2018). This release focuses on adding improved support for recommendation scenarios, model explainability in the form of feature importance, debuggability by previewing your in-memory datasets, API improvements such as caching, filtering, and more.

This blog post provides details about the following topics in the ML.NET 0.8 release:

New Recommendation scenarios

Improved debuggability

Model explainability

Additional API improvements

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Read more: Announcing ML.NET 0.8 Machine Learning for .NET | .NET Blog

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Brink · Dec 4, 2018

Announcing ML.NET 0.7 (Machine Learning .NET)

We’re excited to announce today the release of ML.NET 0.7 – the latest release of the cross-platform and open source machine learning framework for .NET developers (ML.NET 0.1 was released at //Build 2018). This release focuses on enabling better support for recommendation based ML tasks, enabling anomaly detection, enhancing the customizability of the machine learning pipelines, enabling using ML.NET in x86 apps, and more.

This blog post provides details about the following topics in the ML.NET 0.7 release:

Enhanced support for recommendation tasks with Matrix Factorization

Enabled anomaly detection scenarios – detecting unusual events rel

Improved customizability of ML pipelines

x86 support

NimbusML – experimental Python bindings for ML.NET

Get started with ML.NET v07

Enhanced support for recommendation tasks with Matrix Factorization

Recommender systems enable producing a list of recommendations for products in a catalog, songs, movies, and more. We have improved support for creating recommender systems in ML.NET by adding Matrix factorization (MF), a common approach to recommendations when you have data on how users rated items in your catalog. For example, you might know how users rated some movies and want to recommend which other movies they are likely to watch next.

We added MF to ML.NET because it is often significantly faster than Field-Aware Factorization Machines (which we added in ML.NET 0.3) and it can support ratings which are continuous number ratings (e.g. 1-5 stars) instead of boolean values (“liked” or “didn’t like”). Even though we just added MF, you might still want to use FFM if you want to take advantage of other information beyond the rating a user assigns to an item (e.g. movie genre, movie release date, user profile). A more in-depth discussion of the differences can be found here.

Sample usage of MF can be found here. The example is general but you can imagine that the matrix rows correspond to users, matrix columns correspond to movies, and matrix values correspond to ratings. This matrix would be quite sparse as users have only rated a small subset of the catalog.

ML.NET’s MF uses LIBMF.

Enabled anomaly detection scenarios – detecting unusual events

Anomaly detection enables identifying unusual values or events. It is used in scenarios such as fraud detection (identifying suspicious credit card transactions) and server monitoring (identifying unusual activity).

ML.NET 0.7 enables detecting two types of anomalous behavior:

Spike detection: spikes are attributed to sudden yet temporary bursts in values of the input data. These could be outliers due to outages, cyber-attacks, viral web content, etc.

Change point detection: change points mark the beginning of more persistent deviations in the behavior of the data. For example, if product sales are relatively consistent and become more popular (monthly sales double), there is a change point when the trend changes.

These anomalies can be detected on two types of data using different ML.NET components:

IidSpikeDetector and IidChangePointDetector are used on data assumed to be from one stationary distribution (each data point is independent of previous data, such as the number of retweets of each tweet).

SsaSpikeDetector and SsaChangePointDetector are used on data that has a season/trend components (perhaps ordered by time, such as product sales)

Sample code using anomaly detection with ML.NET can be found here.

Improved customizability of ML.NET pipelines

ML.NET offers a variety of data transformations (e.g. processing text, images, categorical features, etc.). However, some use cases require application-specific transformations, such as calculating cosine similarity between two text columns. We have now added support for custom transforms so you can easily include custom business logic.

The CustomMappingEstimator allows you to write your own methods to process data and bring them into the ML.NET pipeline. Here is what it would look like in the pipeline:

var estimator = mlContext.Transforms.CustomMapping<MyInput, MyOutput>(MyLambda.MyAction, "MyLambda")
.Append(...)
.Append(...)

Below is the definition of what this custom mapping will do. In this example, we convert the text label (“spam” or “ham”) to a boolean label (true or false).

public class MyInput
{
public string Label { get; set; }
}
public class MyOutput
{
public bool Label { get; set; }
}
public class MyLambda
{
[Export("MyLambda")]
public ITransformer MyTransformer => ML.Transforms.CustomMappingTransformer<MyInput, MyOutput>(MyAction, "MyLambda");
[Import]
public MLContext ML { get; set; }
public static void MyAction(MyInput input, MyOutput output)
{
output.Label= input.Label == "spam" ? true : false;
}
}

A more complete example of the CustomMappingEstimator can be found here.

x86 support in addition to x64

With this release of ML.NET you can now train and use machine learning models on x86 / 32-bit architecture devices. Previously, ML.NET was limited to x64 devices.

Note that some components that are based on external dependencies (e.g. TensorFlow) will not be available in x86.

NimbusML – experimental Python bindings for ML.NET

NimbusML provides experimental Python bindings for ML.NET. We have seen feedback from the external community and internal teams regarding the use of multiple programming languages. We wanted to enable as many people as possible to benefit from ML.NET and help teams to work together more easily. ML.NET not only enables data scientists to train and use machine learning models in Python (with components that can also be used in scikit-learn pipelines), but it also enables saving models which can be easily used in .NET applications through ML.NET (see here for more details).

In case you missed it: provide your feedback on the new API

ML.NET 0.6 introduced a new set of APIs for ML.NET that provide enhanced flexibility. These APIs in 0.7 and upcoming versions are still evolving and we would love to get your feedback so you can help shape the long-term API for ML.NET.

Want to get involved? Start by providing feedback through issues at the ML.NET GitHub repo!

Additional resources

The most important ML.NET concepts for understanding the new API are introduced here.

A cookbook (How to guides) that shows how to use these APIs for a variety of existing and new scenarios can be found here.

A ML.NET API Reference with all the documented APIs can be found here.

Get started!

If you haven’t already, get started with ML.NET here Next, explore some other great resources:

Tutorials and resources at the Microsoft Docs ML.NET Guide

Code samples at the machinelearning-samples GitHub repo

We look forward to your feedback and welcome you to file issues with any suggestions or enhancements in the ML.NET GitHub repo.

This blog was authored by Gal Oshri and Cesar de la Torre

Thanks,

The ML.NET Team
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Source: Announcing ML.NET 0.7 (Machine Learning .NET) | .NET Blog

Brink · Dec 4, 2018

Announcing .NET Framework 4.6

We're excited to announce the RTM releases of .NET Framework 4.6 and Visual Studio 2015 today. You can read about the new features or leave that for later and try them out now. The quickest way to get started is to install the free Visual Studio 2015 Community version.

With the .NET Framework 4.6, you'll enjoy better performance with the new 64-bit "RyuJIT" JIT and high DPI support for WPF and Windows Forms. ASP.NET provides HTTP/2 support when running on Windows 10 and has more async task-returning APIs. There are also major updates in Visual Studio 2015 for .NET developers, many of which are built on top of the new Roslyn compiler framework. The .NET languages -- C# 6, F# 4, VB 14 -- have been updated, too.

We're also announcing updates to .NET Core and ASP.NET 5, both recently released as beta 5 and included in Visual Studio 2015. The .NET tools for Windows 10 UWP app development, including .NET Native, will be shipping shortly, on 7/29. We will have a lot to share about .NET UWP apps and the .NET Native technology at that time.

You can download and try out the releases now:

Visual Studio 2015

.NET Framework 4.6

As a team, we're really excited to share everything we've been working on:

.NET Framework 4.6

ASP.NET 4.6

Entity Framework

.NET Languages

Visual Studio Improvements for .NET Developers

.NET Core and ASP.NET 5

You can check out the earlier RC and Preview releases to see how the release has developed over the last year. In fact, it's only been 14 months since we released the .NET Framework 4.5.2.

.NET Framework 4.6

There are many great features in the .NET Framework 4.6. Some of these features, like RyuJIT and the latest GC updates, can provide improvements by just installing the .NET Framework 4.6. Give it a try!

You can learn more about the release by looking at What's New in the .NET Framework, the .NET Framework 4.6 release changelist and an .NET Framework API diff between the .NET Framework 4.6 and 4.5.2 releases. Check out the ASP.NET Team post to learn more about ASP.NET updates.

The .NET Framework 4.6 is part of Windows 10 and can be installed on Windows 7 and Windows 8. You can target the .NET Framework 4.6 in Visual Studio 2012 or later, by installing the the .NET Framework 4.6 Targeting Pack. It comes with Visual Studio 2015...
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Read more: Announcing .NET Framework 4.6 - .NET Blog - Site Home - MSDN Blogs

Brink · Dec 4, 2018

Announcing .NET Standard 2.0

The .NET Standard 2.0 specification is now complete. It is supported in .NET Core 2.0, in the .NET Framework 4.6.1 and later versions, and in Visual Studio 15.3. You can start using .NET Standard 2.0 today.

For the impatient: TL;DR

.NET Standard is for sharing code. .NET Standard is a set of APIs that all .NET implementations must provide to conform to the standard. This unifies the .NET implementations and prevents future fragmentation. It replaces Portable Class Libraries (PCLs) as the tool for building .NET libraries that work everywhere.

Much bigger API Surface: We have more than doubled the set of available APIs from 13k in .NET Standard 1.6 to 32k in .NET Standard 2.0. Most of them are existing .NET Framework APIs. These additions make it much easier to port existing code to .NET Standard, and, by extension, to any .NET implementation of .NET Standard, such as .NET Core 2.0 and the upcoming version of UWP.

.NET Framework compatibility mode: The vast majority of NuGet packages are currently still targeting .NET Framework. Many projects are currently blocked from moving to .NET Standard because not all their dependencies are targeting .NET Standard yet. That’s why we added a compatibility mode that allows .NET Standard projects to reference .NET Framework libraries. While this may not work in all cases (for instance, if the .NET Framework binaries use WPF) we found that with .NET Standard 2.0. So in practice it unblocks many projects.

Broad platform support. .NET Standard 2.0 is supported on the following platforms:

.NET Framework 4.6.1

.NET Core 2.0

Mono 5.4

Xamarin.iOS 10.14

Xamarin.Mac 3.8

Xamarin.Android 7.5

Upcoming version of UWP (expected to ship later this year)

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Read more: Announcing .NET Standard 2.0 | .NET Blog

Windows 10: Announcing ML.NET 0.8 - Machine Learning for .NET

Announcing ML.NET 0.8 - Machine Learning for .NET

Announcing ML.NET 0.8 - Machine Learning for .NET

Announcing ML.NET 0.8 - Machine Learning for .NET

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