TF Lite Mobile Deployment Expert
Guides the end-to-end deployment of TensorFlow Lite models on mobile and edge devices — from model conversion and quantization to on-device inference optimization, hardware delegate selection, and production monitoring. Ensures models meet latency, size, and accuracy constraints for resource-constrained environments.
SupaScore
83.95Best for
- ▸Converting PyTorch/TensorFlow models to TensorFlow Lite format with optimal quantization strategy
- ▸Implementing INT8 post-training quantization with representative datasets for mobile inference
- ▸Configuring hardware delegates (NNAPI, GPU, CoreML) for Android/iOS deployment optimization
- ▸Debugging TFLite model conversion errors and unsupported operations
- ▸Setting up production monitoring for on-device model performance and accuracy drift
What you'll get
- ●Step-by-step TFLite conversion script with quantization configuration, representative dataset preparation, and conversion error resolution
- ●Hardware delegate implementation code for Android/iOS with performance benchmarking and fallback strategies
- ●Production deployment checklist with model size optimization, latency targets, and A/B testing framework for mobile ML features
Not designed for ↓
- ×Training machine learning models from scratch or model architecture design
- ×Native Android/iOS app development unrelated to ML inference
- ×Server-side model serving or cloud deployment strategies
- ×Computer vision or NLP algorithm development
A trained model (TensorFlow SavedModel, Keras, or ONNX), target mobile platform specifications, and performance constraints (latency, model size, accuracy thresholds).
Optimized TFLite model with quantization configuration, hardware delegate setup code, performance benchmarks, and production deployment recommendations.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (4 official docs, 3 academic, 1 community practice)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
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Works well with
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Common Workflows
Mobile ML Pipeline Deployment
Train model, optimize for mobile deployment, and integrate into mobile app UX
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