Deploy a custom Machine Learning model to mobile
summary
Walks from a custom TensorFlow model through conversion, optimization, benchmarking, and mobile deployment using concrete tools: TensorFlow Lite converter produces a .tflite file, the TensorFlow Model Optimization Toolkit applies post‑training quantization, and Quantization Debugger pinpoints a bad layer so selective quantization skips it to recover accuracy. CPU speed ups come from XNNPACK (on by default since TFLite 2.7); delegates (GPU, NNAPI, Core ML) and the benchmarking tool measure real device latency and identify slow ops before shipping via Firebase or Play services.
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chapters (6)
- 0:00Introduction and goals
- 2:10Model discovery and conversion
- 4:25Understanding TFLite architecture
- 7:07Model optimization techniques
- 10:10Benchmarking and performance
- 13:48Deployment and maintenance
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