Why On-Device ML
On-device machine learning offers significant advantages: zero latency for real-time predictions, works offline without internet connection, enhanced privacy by keeping data on device, reduced server costs, and better user experience. However, it comes with constraints: limited model size, reduced accuracy compared to cloud models, and platform-specific implementations.
Model Optimization Techniques
Optimize models for mobile deployment through pruning to remove unnecessary weights, quantization to reduce precision from float32 to int8, knowledge distillation to create smaller student models, and architecture search to find mobile-optimized structures. Target model size under 10MB for optimal app size and download experience.
Quantization can reduce model size by 75% with minimal accuracy loss, making it essential for mobile deployment.
TensorFlow Lite Implementation
Convert TensorFlow models to TFLite format using TFLiteConverter. Add TensorFlow Lite dependency to your Android or iOS project, load the model from assets, create an Interpreter instance, prepare input tensors with correct shape and type, run inference, and parse output tensors. Use GPU delegate for faster inference on supported devices.
Core ML Integration
Convert models to Core ML format using coremltools. Add the .mlmodel file to Xcode project, use automatically generated Swift/Objective-C classes, prepare input as CVPixelBuffer or MLMultiArray, call prediction method, and handle output. Core ML automatically optimizes for Apple Neural Engine on A12+ chips.
Quantization and Compression
Post-training quantization converts weights to 8-bit integers without retraining. Quantization-aware training simulates quantization during training for better accuracy. Dynamic range quantization reduces model size, full integer quantization enables integer-only inference for maximum speed, and float16 quantization balances size and accuracy.
Inference Performance
Optimize inference by running on background threads to avoid blocking UI, batching multiple predictions when possible, caching model in memory, using hardware acceleration (GPU, NPU), and implementing proper memory management to prevent leaks. Monitor inference time and adjust model complexity if needed.
Fallback Strategies
Implement cloud fallback for edge cases where on-device model confidence is low, device doesn't support required operations, or model is too large for device. Use hybrid approach: fast on-device inference with cloud validation for critical decisions. Update models via remote config without app updates.