Let’s Take a Look at ML.NET

ML.NET is nothing but a machine learning framework which is open source in nature. This has been created for the convenience of .NET developers by Microsoft. It is cross-platform and you can run it on Windows, macOS and Linux. The popularity of .NET is high in the market and so planning on hiring ML.NET developers to build customized machine learning models can be a great idea. Such models are developed and integrated into .NET apps which can be anything ranging from desktop mobile, web and even IoT.

There are a few reasons which make ML.NET right for building custom machine learning models and the same has been explained below:

Being open source, .NET foundation backs ML.NET. This is why ML.NET is still in the preview state. Another important feature of ML.NET is its cross-platform compatibility.

In ML.NET you can find a number of Microsoft Research developed machine learning libraries. So if you want, you can use them in any of the other Microsoft products. Again, by using the extensible platform it is possible for you to have some other libraries like TensorFlow, Accord.NET and CNTK used.

Using ML.NET, now you can have a single pipeline for data loading, transformations and model training. This means for your input data and training data you can have the transformations properly defined within the pipeline. You can use this to make predictions for the trained models of yours.

It is right to make use of transforms set which is built-in if you want to get your data in preferred types and format. Again, for the purpose of processing you need them. In the normal scenario, ML.NET offers support for:

• Changing Data Schema
• Ngram Featurization
• Normalization
• Text Transforms
• Categorical Variable Encoding
• Selecting Relevant Training features

By using ML.NET, you can load the following kinds of data into the pipeline:

• Parquet
• File Sets
• Binary
• Text
• IEnumerable

You should consider learning that algorithm which can be considered as the best keeping the current scenario in mind. In the normal scenario, ML.NET offers the following learners:

• SVM
• Boosted Trees
• K-Means
• Linear
• Averaged Perceptron

ML.NET also offers evaluators. You can use them to evaluate the performance of model based on large number of metrics. Again, you get to choose the appropriate evaluator based on your machine learning.

Train method can be called to involve your model in training. Then the said model in question will return the prediction model object. This object makes use of input types and your output in order to make predictions.

In the ML.NET, you can save the trained model as binary file. This means into the .NET application it is possible to for you to integrate:-

var prediction = model.Predict(inputData);

var model = PredictionMolde.ReadAsync(modelPath).Result;