How to Transform Machine Learning Models into Native Code with Zero Dependencies

How to Transform Machine Learning Models into Native Code with Zero Dependencies

Learn how to use the m2cgen Python library to convert the trained machine learning model into native code (for example Python, PHP, or JavaScript) with zero dependencies. m2cgen (Model 2 Code Generator) is a simple Python library that converts a trained machine learning model into different programming languages. M2cgen supports 14 different programming languages: C, C#, Dart, F#, Go, Haskell, Java, JavaScript, PHP, PowerShell, Python, R, Ruby, Visual Basic (VBA-compatible)

Most trained machine learning models are saved as pickle files. This file type is the standard way of serializing and de-serializing objects in Python.

In order to make predictions, you need to load the saved trained model and then perform predictions from the inputs provided.

In this article you will learn how to use the m2cgen Python library to convert the trained machine learning model into native code (for example Python, PHP, or JavaScript) with zero dependencies. Then you'll make predictions based on it.

What is the m2cgen Python Library?

m2cgen (Model 2 Code Generator) is a simple Python library that converts a trained machine learning model into different programming languages.

For example, you can train your machine learning model from the Scikit-learn library and then convert it into the programming language of your choice.

This library is very useful if you want to deploy models into environments where you can't install your Python stack to support your model prediction.

Languages supported by the m2cgen library

M2cgen supports 14 different programming languages:

  • C
  • C#
  • Dart
  • F#
  • Go
  • Haskell
  • Java
  • JavaScript
  • PHP
  • PowerShell
  • Python
  • R
  • Ruby
  • Visual Basic (VBA-compatible)

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