<p>As iOS engineers, we respect the science of training models, but we live in the trenches of inference. In this episode, we explore the "Great Divide" between creating a model and running it. We break down the memory mechanics of O(N) training vs O(1) inference, dissect compiler optimizations like kernel fusion, and explain exactly how the Apple Neural Engine cheats bandwidth physics using quantization.</p>

As iOS engineers, we respect the science of training models, but we live in the trenches of inference. In this episode, we explore the "Great Divide" between creating a model and running it. We break down the memory mechanics of O(N) training vs O(1) inference, dissect compiler optimizations like kernel fusion, and explain exactly how the Apple Neural Engine cheats bandwidth physics using quantization.

Machine Learning Engineering: Training vs. Inference

Sandboxed is a podcast for iOS developers who want to add AI and machine learning features to their apps—without needing a PhD in ML. Each episode, we take one practical topic—like Vision, Core ML, or Apple Intelligence—and walk through how it works, what you can build with it, and how to ship it.

Share Machine Learning Engineering: Training vs. Inference

Sign up to save your podcasts

Machine Learning Engineering: Training vs. Inference

Machine Learning Engineering: Training vs. Inference