Machine Learning and Artificial Intelligence are two of the most trending technologies. We have moved past the practiced approach of science and technology where problem-solving was based on some tested and successful approaches. With advanced technologies like Machine Learning (ML), we have created our problem-solving models. Owing to this specific potential, more and more people are delving into ML and deep learning.

What is ML?

Machine Learning is the branch of Artificial Intelligence that offers intelligence to solve problems. Unlike conventional programming, it is a mathematical model based on the data and related examples. Data from different sources act as the input to this model. This data is then processed and structured to be fed into the ML algorithm. This algorithm in turn makes certain predictions based on different parameters which are considered as the output.

Many of us might have an ML model available to us. We might be waiting to convert this model into a web application and showcase it to everyone else. Let us look into the solution.

How to Convert a ML Model into a .NET Application?

You are not on the wrong track if you are going to hire .NET developers. You can easily achieve this with Microsoft’s Machine Learning Framework ML.NET. This is an end-to-end tool that enables you to bind different data

The technical requirement to use ML.NET is installing .NET3 or .NET 5 and along with a 64-bit processor. .NET SDK is the next thing that you need to install. The installation of ML.NET is pretty simple using the Package Manager Console. We need to run a command:

Install-Package Microsoft.ML

The next step is

dotnet add package Microsoft.ML

Similarly, we can install Microsoft.ML.DataView using the console. Another way of doing the same is using the Manage NuGetPackage option available in Visual Studio.

Steps to Build Application with ML.NET

1. Load data: Start with loading the raw data into the memory using IDataView.

2. Create Pipeline: Here we transform the data using the transformational steps provided by ML.NET, making it suitable for ML algorithm.

3. Train an ML Model: Users can use the Fit() method to train the Machine Learning model. 

4. Save the ML model: Now that we have trained the model, save this ML model in a file. 

5. Load the ML model: The final step is loading this ML model to make predictions. 

These are the basic steps. You will come to know more details and components such as Estimator after getting into practical application.

Conclusion

Building a .NET application using an ML model gets interesting with ML.NET. However, it can be difficult and complex for newbies. It would be a great idea to hire .NET services to get the best and most appropriate solution. We are one of the leading application development companies that can help you with all your application and software-related requirements. Contact us today to get a cost-effective quote for your application development requirements.