Implementing Artificial Intelligence with .NET

This article talks about some of the tools, frameworks, and libraries that support AI and ML with .NET.

Artificial Intelligence has now become a buzzword among developers. The developers could develop their apps for any platform including mobiles, desktops and web platforms. Artificial Intelligence has made a niche for itself. We rarely find any apps today, that are not equipped with AI. The apps are generally developed in languages such as Java when we talk of Android, iOS or even web apps. However, the developers are not restricted to the use of a single language. One developer might have proficiency in Visual Basic, and another in C++. So, how to develop apps when developers are using varied interests? The answer is quite simple – a common platform such as .NET framework that supports 45 plus languages under its canopy.
 
.NET developers have been developing apps for mobiles using C# and Xamarin. Now that Artificial Intelligence is also being incorporated, Microsoft is using C# as well as F# to develop AI-based applications. With Amazon coming up with its own Artificial Intelligence products such as Echo and Alexa , Microsoft is invariably to follow.
 
Today .NET developers have various options using which they can infuse Artificial Intelligence into their applications. These tools can be categorized as: 
  1. Pre-built tools
  2. Deep Learning Using Microsoft Cognitive Toolkit (CNTK)
  3. Azure Machine Learning
  4. Tensor 
Whichever tools the .NET developers choose to build Artificial Intelligence-based apps, they get the following advantages that accrue from the use of the .NET framework: 
  • The .Net developers have the option of either building their own models or, choosing from the in-built API’s that help incorporate AI into an app without the need for sophisticated coding.
  • Being a Microsoft.NET developer has its advantage of using on-thy-fly AI tools such as Cognitive Service Bots, CoreML, & Vision for Xamarin iOS apps and CNTK.
  • Azure Machine Learning, Tensorflow Accort.Net, and CNTK help the .Net developers build their own customized models that make use of the Artificial Intelligence, along with Machine Learning. 

In-Built / Pre-Built Tools

 
A number of Cognitive services provide models that can be used to easily implement the concepts of Artificial Intelligence and Machine Learning in .NET applications. These include open-source algorithms that help the apps to include features such as Vision, Knowledge, Language, and Search. These libraries can be easily used by C# and F# developers in their applications. These days iOS developers are using CoreML & Visual Studio for Xamarin prebuilt machine learning modules for their iOS apps. These prebuilt modules help Apple in developing AI-enabled apps for their iOS devices.
 
 

CNTK

 
 
Microsoft’s Deep Learning library,which is also open-source, enables Deep Learning using CNTK. This library is comprised of neural networks such as LSTM, ConvNets, feed-forward brainscript, and other C# deep architectures. CNTK provides binding in Python, and brainscript. It also provides pre-train alex.NET and google.NET to be used for C# applications. CNTK C# applications help in building, training and assessing CNTK modules. Developers can make use of Core ML for Xamarin to make use of pre-trained CNTK modules or, they can make use of their own models, that they have created the help of Azure Machine Learning, Tensorflow, Accord.Net and CNTK.
 
Learn more here, Learn more about CNTK.
 

Azure Machine Learning

 
This is a browser-based studio, where developers can simply generate code, (rather than writing it) by using the drag and drop feature. The studio has a number of packages for enabling data science modules into the applications. Data Science features can also be added by drag and drop. Thus, making it easier for the developers to develop AI-based apps.
 
 

Tensor

 
It is the latest application that is making waves in the .NET circles. Using Tensor, machine libraries such as Tensorflow and CNTK can easily incorporate their APIs into a NET application for enabling Artificial Intelligence. Some features of Tensor are:
  1. Tensor acts as a container for distinctive sorts of multidimensional information on a one-one measurement
  2. It is planned for an optimized exchange type of multidimensional machine-learning information.
  3. It underpins diverse, meager, and thick formats giving efficient interrupt for local libraries such as CNTK, guaranteeing negligible duplicates of information.
  4. Before long it'll be a portion of base class libraries working with any sort of memory wheteher it is unmanaged or managed, permit for slicing and ordering information effectively 
Learn more here, What is TensorFlow.
 

How to Enable Artificial Intelligence and Machine Learning in .NET Applications?

 
Microsoft has been providing its users with very useful concepts, services, tools, and frameworks. ASP.Net is one such tool that has found favor with .NET developers. It is considered to be the platform that can easily provide the best apps to its users. In 2016, Microsoft again came up with two important updates to its .NET Framework. This was the inclusion of the .NET AI-based modules Microsoft Cognitive Services, and the BOT Framework.
 
The BOT framework can be used to create intelligent bots and connect them with various applications that can interact with users whenever and wherever.
 
Microsoft Cognitive Services help the developers to include features such as Vision, Knowledge, Language, and Search which is based on Artificial Intelligence.
 
Recently, Microsoft has upgraded its .NET Framework to CORE.NET. This version of the .NET Framework has enabled the developers to develop a RAD application that is AI-enabled. The Web-part of this framework i.e. ASP.NET now comes with the following features: 
  1. Flexibility is Added by Making it Open Source: Due to its open-source nature, it permits developers to preserve the modularity over different improvement environment,for simplifying the source code. In addition, it boosts the adaptability of extra system libraries and components which are required for application advancement.
  2. Cross-Platform Functionality: Mac and Linux. ASP.NET services in various organizations allows users to incorporate one application, which can be run on a diverse number of platforms. As of now, this feature is available only in CORE.NET and not standard .NET Framework.
  3. Gives Support for Hosting Independence: Amid the improvement of an application, these applications can run on different web servers other than the IIS (Internet Information Services). Since ASP.NET Core MVC bolsters cross-platform usefulness, it cannot keep any application dependent on the IIS server.
  4. Improved Support for Cloud Deployment: Due to the secluded design of the system, it has upgraded the bolster for cloud deployment. The extended modularity and adaptable environment given by ASP.NET Core MVC, empowers it, creating modern applications which are however to be deployed on the cloud.