From model to app.
A model that works on a computer often doesn't fit in a phone. Here you learn to measure it, shrink it and run it without a network.
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06.01
Measuring for real
18 min
Confusion matrix, precision and recall, latency, memory and battery, measured on an average phone. The threshold and the two errors
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06.02
Quantizing
16 min
From 32 to 8 and then 4 bits. How much space you save and how much accuracy you lose. How many bits do you need?
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06.03
Pruning, distilling, choosing the architecture
16 min
Removing the weights you don't need, and having a small model learn from a big one.
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06.04
Formats and engines
18 min
LiteRT, Core ML, ONNX Runtime, ExecuTorch, MediaPipe and WebGPU. Which ones use the NPU, and when.
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06.05
An app that works without a network
20 min
Installable apps, privacy, updates and models that learn on the device. Lab, final step
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06.06
Responsible AI
18 min
Who answers for the mistakes, data that leave some people behind, consent, the limits to tell people and the European rules.
The course review → The key concepts and new questions, once you've finished the lessons.