

Deploying Edge and Embedded AI Systems with Heather Gorr - #655
Nov 13, 2023
Heather Gorr, Principal MATLAB Product Marketing Manager at MathWorks, dives into the fascinating world of deploying AI models for embedded systems. She emphasizes crucial factors like data preparation, device constraints, and latency requirements for successful implementation. Heather shares insights on MLOps techniques to enhance deployment speed, while tailoring AI solutions for industries such as automotive and oil & gas. Anecdotes of real-world AI applications illustrate the importance of rigorous validation processes and interdisciplinary collaboration in ensuring safety and reliability.
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Physics-Based Models Supplement Data
- Baker Hughes used physics-based models for predictive maintenance on pumps.
- This supplemented data for training AI models, addressing the challenge of limited failure data.
Mercedes-Benz Multi-Tool Approach
- Mercedes-Benz combined MATLAB simulations, Python deep learning models (PyTorch), and quantization.
- They integrated these diverse tools for sensor simulation and deployment on hardware devices.
Consider Device Limitations First
- Start with device limitations before model development.
- Consider data volume, processing time, and latency to ensure compatibility with hardware.