Interoperability Part 2: Unifying MATLAB and Python for AI

April 2, 2024
In Part 2 of this series, MathWorks' Heather Gorr details how to take advantage of Python-based AI and ML libraries via MATLAB.

What you’ll learn:

  • Why interoperability between multiple programming languages is important. 
  • How MATLAB integrates with AI platforms like TensorFlow and PyTorch. 

MATLAB supports a range of programming languages. The challenge is mixing them. I talked with Heather Gorr, PhD, Principal MATLAB Product Marketing Manager at MathWorks, about using MATLAB with Python to deal with applications that take advantage of Python-based artificial-intelligence (AI) and machine-learning (ML) libraries (see the video above). The other two parts of the series are listed below. 

Why Interoperability Between Programming Languages is Important

Interoperability Part 1: Using Multiple Programming Languages

MathWorks' Heather Gorr talks about interoperability issues that arise when using multiple programming languages.
Integrating Two Programming Languages in Practice

Interoperability Part 3: Integrating Two Programming Languages in Practice

Wrapping up this video series, MathWorks' Heather Gorr talks about development of projects with multiple programming languages.

One of the reasons for using different programming languages is to gain access to the support or services they may offer. For example, MATLAB is very easy to use and provides excellent support for developing algorithms with plenty of matrices.

It's possible to do just about anything with MATLAB, but many frameworks for things like AI and ML, including PyTorch, rely on programming languages such as Python for their native specifications (see figure).

Mixing and matching programming languages, tools, and libraries enables interdisciplinary teams to use the tool they find most effective while sharing those applications among the group.  


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