MATLAB

MATLAB and Simulink for Artificial Intelligence

Transform engineering and science with AI

MATLAB is an engineering platform for integrating AI in the design, development, and operationalization of engineered systems. Engineers and scientists use MATLAB to create better AI datasets, build AI models for domain-specific tasks, and continuously test these models in a system-wide context.

Experience the transformative ways engineers and scientists use MATLAB for AI to boost existing workflows and invent solutions within complex environments.

Reliable Tools for AI-Driven Systems

AI is an emerging and rapidly evolving technology. MATLAB empowers engineers and scientists to use AI in their domain and enables collaboration across teams and organizations.

With MATLAB, you can:

  • Create AI models with a few lines of code or use pretrained models
  • Use domain-specific tools and low-code apps to build complete and scalable AI workflows
  • Combine AI techniques with system-level simulation to reduce errors in production
  • Deploy AI models to high-performance systems, such as edge devices and the cloud
  • Exchange AI models and design functionality between MATLAB and Python
DL

Deep Learning

Design, simulate, and deploy systems with deep neural networks.
ML

Machine Learning

Train models, tune parameters, and deploy to production or the edge.
RL

Reinforcement Learning

Define, train, and deploy reinforcement learning policies.

Apply AI to Your Domain

Whether you are new to AI or developing your skills, MATLAB lets you integrate AI into various application workflows such as robotics, predictive maintenance, and many more.

COMPUTER VISION

Predictive Maintenance

LIDAR

WIRELESS

ELECTRIFICATION

RADAR

SIGNAL PROCESSING

ROBOTIC

CONTROL ALGORITM

Featured Application: Visual Inspection

Use computer vision to detect anomalies in images automatically. AI applications such as visual inspection require a systematic approach to:

  • Improve the quality of the training data with automatic labeling, data cleaning, and synthetic data generation
  • Achieve prediction accuracy with the AI model that is suitable for production deployment
  • Test the integration of the AI model with other parts of the system