Deploying Edge and Embedded AI Systems with Heather Gorr - #655
Published:Nov 13, 2023 18:56
•2 min read
•Practical AI
Analysis
This article from Practical AI discusses the deployment of AI models to hardware devices and embedded AI systems. It features an interview with Heather Gorr, a principal MATLAB product marketing manager at MathWorks. The conversation covers crucial aspects of successful deployment, including data preparation, model development, and the deployment process itself. Key considerations like device constraints, latency requirements, model explainability, robustness, and quantization are highlighted. The article also emphasizes the importance of simulation, verification, validation, and MLOps techniques. Gorr shares real-world examples from industries like automotive and oil & gas, providing practical context.
Key Takeaways
- •The article focuses on the practical aspects of deploying AI models to edge and embedded systems.
- •It highlights key considerations like device constraints, latency, model explainability, and the importance of MLOps.
- •Real-world examples from industries like automotive and oil & gas are provided to illustrate the concepts.
Reference
“Factors such as device constraints and latency requirements which dictate the amount and frequency of data flowing onto the device are discussed, as are modeling needs such as explainability, robustness and quantization; the use of simulation throughout the modeling process; the need to apply robust verification and validation methodologies to ensure safety and reliability; and the need to adapt and apply MLOps techniques for speed and consistency.”