Standard 3 stops to get here
Edge Deployment
Running models on edge devices (phones, IoT) rather than cloud servers for lower latency and privacy.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Inference ✓ understood
Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.
- Edge Deployment · you are here ✓ understood
Where it sits
Explore nearby
Shipping AI Quantization Reducing model precision (FP32 → INT8) to decrease size and increase inference speed with minimal accuracy loss. Shipping AI Model Compression Techniques to reduce model size and computational requirements (quantization, pruning, distillation) for efficient deployment. Shipping AI Pruning Removing unnecessary weights or neurons from a trained model to reduce size and computation while maintaining performance. Shipping AI Inference Latency The time delay between submitting input and receiving output from a deployed model, critical for real-time applications. Shipping AI Federated Learning Training models across decentralized devices holding local data, without exchanging the data itself, preserving privacy.