Hi Google AI community,
I have been exploring AI applications that run closer to users through edge devices and looking into the challenges involved in deploying machine learning models outside traditional cloud environments.
Running AI on edge devices introduces different considerations such as:
- Model size and optimization
- Inference performance and latency
- Hardware limitations
- Quantization and accuracy trade-offs
- Updating and maintaining deployed models
I would like to learn from developers working with Google AI Edge technologies:
- What approaches do you use to optimize models for edge deployment?
- How do you balance model performance with resource limitations?
- What are the biggest challenges you have faced when deploying AI models on mobile or edge devices?
Would appreciate any practical experiences and recommendations from the community.