We explain why a .mlmodel is not code, but a "Frozen Artifact"—a factory built from experience, welded shut, and shipped to your user.
🏭The Factory Analogy
Most iOS developers treat Machine Learning models like black boxes. But models are immutable assembly lines.
"Training" is the act of building the factory and adjusting the dials. "Inference" is simply turning the conveyor belt on. Once shipped, the factory is welded shut.
📦The Anatomy of a Model
We deconstruct the .mlmodelc folder:
- Structure: The graph of operations.
- Weights: The frozen knowledge (Sensitivity).
- Metadata: The manual for the inputs and outputs.
🔧Integration Best Practices
Integration is not "plug and play." Meaningful AI integration usually takes 3x longer than the model selection process.
Use the Vision Framework (VNCoreMLRequest) instead of raw model prediction to handle image resizing and memory management efficiently.
🎯Key Takeaways
- •Models are Assets, Not Code. Manage them like 4K textures or databases.
- •The Model is Frozen. On-device inference is read-only.
- •Quantization is King. Float32 to Int8 saves massive space with negligible loss.
- •Unified Memory is the secret weapon for Zero-Copy performance.
About Sandboxed
Sandboxed is a podcast for people who actually ship iOS apps and care about how secure they are in the real world.
Each episode, we take one practical security topic—like secrets, auth, or hardening your build chain—and walk through how it really works on iOS, what can go wrong, and what you can do about it this week.