ML.NET | Machine Learning Made For .NET | Concetto Labs

ML.NET | Machine Learning Made For .NET

Author image By Tejas Patel  |  Thursday, February 28, 2019 02:49 PM  |  3 min read  |   4

Machine Learning

Machine Learning made for .NET is usually considered to be an open-source as well as a cross-platform machine learning framework. This Microsoft’s ML.NET is again well supported on macOS, Linux and also Windows.

So, it is preferably known to be a very important machine learning framework that is built for the .NET developers. It is also possible to use your .NET as well as C# or F# skills in order to perfectly and easily integrate custom machine learning into your application.

Thus, it does not require any sorts of prior expertise in effectively tuning or developing machine learning models.

What Is ML.NET?

The ML.NET is known to be an open-source machine learning framework that is created by Microsoft mainly for the .NET developer platform. So, it is an important cross-platform which runs on Linux, macOS as well as Windows.

Due to the rising importance and popularity of the .NET, you can hire ML.NET developers.

Custom Machine Learning Models

This ML.NET is preferably used to integrate as well as develop some custom machine learning models into the .NET apps of any type such as mobile, web, gaming, desktop and also IoT. So, you should hire .NET developer if you desire to ease the work.

1. Extensible

Mainly, the ML.NET consists of the machine learning libraries which are created by Microsoft Research. So, it can be used effectively by Microsoft products.

Also, it is possible for you to leverage some other popular libraries such as CNTK, Accord.NET as well as TensorFlow through the extensible platform.

2. Open Source

ML.NET is usually backed by the .NET Foundation as it is open-source. This is the reason why it is currently in preview.

3. Learning Pipelines

ML.NET helps to combine transformations, data loading as well as model training mainly into a single pipeline. So, there are transformations effectively defined in your pipeline which are best applied to both your input data as well as your training data for the purpose of making predictions with your trained model.

4. Load Data

The followed mentioned kinds of data can be loaded into your pipeline by the help of ML.NET:

  • File Sets

  • IEnumerable<Τ>

  • Binary

  • Parquet

  • Text (CSV/TSV)

5. Transform Data

To preferably get your data into the proper format as well as types, it becomes very much important to use the built-in set of the transforms. These are again needed for the purpose of processing. It is evident that ML.NET generally offers support for:

  • Ngram Featurization

  • Selecting Relevant Training Features

  • Normalization

  • Categorical Variable Encoding

  • Handling Missing Data Values

  • Changing Data Schema

  • Text Transforms

Choose Algorithm

You should effectively choose that learning algorithm which will provide you with the highest accuracy for your scenario. Usually, the following types of learners are offered by the ML.NET such as:

  • Boosted Trees (e.g. FastTree, LightGBM)

  • Linear (e.g. SymSGD, SDCA)

  • K-Means

  • Averaged Perceptron

  • SVM

1. Train Model

Usually, by calling the Train method, you should involve in training your model. Also, a prediction model object will then be returned by this particular method which mainly uses both your output as well as input types in order to make predictions.

2. Evaluate Model

ML.NET is also particularly involved in offering evaluators which contribute to access your model performance on several varieties of the metrics. Also, depending on your machine learning, you have the option to choose the appropriate evaluator.

3. Deploy Model

It is the ML.NET which preferably allows you to save your trained model as a binary file. So, it is preferably possible for you to integrate into any of the .NET application.

var model = PredictionModel.ReadAsync(model path).Result;

var prediction = model.Predict(inputData);

Open Source And Cross-Platform

Both of this i.e. open source as well as cross-platform are considered to be a very important feature of the ML.NET.

Extensible And Proven

Other than that, it is usually considered to be proven as well as extensible. To power your own applications, it is usually possible for you to use the same framework behind some of the Microsoft features such as Bing Ads, Windows Hello and also PowerPoint Design Ideas.

Gradually, the ML.NET will become an extensible framework with the particular support for Accord.NET, LightGBM, TensorFlow as well as CNTK coming very soon.

ML.NET

Conclusion

So, it should be evident from the above-given facts that the machine learning made for .NET has become much popular. Also, you need to be well aware of the basics of machine learning and the way in which it is possible to integrate as well as develop some of the custom machine learning models into your applications by the usage of ML.NET.

Originally Published by c-sharpcorner

 


Author image

Tejas Patel

Tejas Patel is a Co-Founder of Concetto Labs, I have 12+ years of professional experience in building high quality and scalable web applications using FullStack & PHP with its different frameworks.

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