Matlab vs. Python? Interesting question. Thousands of blogs and studies are being published daily in pursuit of providing the best comparison. But here is an even better question: why do you have to choose if you can get the best of both?

Matlab and Python are both powerful tools for developing AI solutions that can do quite different and incredible things. By taking advantage of the best capabilities of each environment, you can achieve exceptional results.

During this video, we will discuss the interoperability between MATLAB and Python for AI development and deployment collaboration.

Topics include:

1. Set-Up

• MATLAB Engine API for Python Overview
• How to integrate someone else MATLAB code (from the library or collaborator)
• How to debug MATLAB Code in your Python Application

2. Passing Data

• How to store & transfer tabular data between languages
• How to access pandas dataframe
• How to exchange between MATLAB and Python

3. AI Workflow

• Data Preparation
• Modeling & Training
• Simulation & Test
• Deployment

4. Deployment

• How to generate Python library from MATLAB functions
• MATLAB Production Server

5. Calling Python from MATLAB

Watch this video to:

• Gain a clear overview of interoperability between MATLAB and Python with a focus on AI.
• Learn how to work with Python code from within MATLAB
• Learn how to call Python AI models from MATLAB
• Learn how to deploy MATLAB AI algorithms into a mixed-language environment

Learn more

To delve deeper into the topics covered in this video, explore the following resources:

• Blog: MATLAB and Simulink AI Capabilities.
  Dive into the AI prowess of MATLAB and Simulink. Explore how these platforms empower engineers to create AI-driven solutions. Learn about workflow essentials, benefits, and real-world integration.
  Read the blog.
• Blog: Efficient Problem-Solving with ROM and Neural Networks.
  Master Reduced Order Modeling (ROM) and Neural Networks in MATLAB. Discover quick problem-solving strategies, maximizing your efficiency in engineering tasks.
  Read the blog.
• On-demand webinar: Deep Learning for Fault Detection. Part 1 – Sound Analysis.
  Understand the fundamentals of applying deep learning to analyze machine sounds for fault detection. Explore the process of creating and training models tailored for sound data, and learn how to deploy these models for continuous fault detection in real-world scenarios.
  Watch the video.