looks to me like the docs don't give a feature-parity table, but they do draw the "role" lines once you read across them:

- Core ML narrows to classic, non-neural ML (its own docs now point you there for "decision trees or tabular feature engineering")

- Core AI takes neural nets and transformers (the new .aimodel format, the new profiler)

- MLX stays the separate bring-your-own-weights track (its WWDC sessions draw no line back to Core AI at all)

coreai-opt is the successor to coremltools on the optimization side.

My reading of it is:

- Core ML is for models designed only for Apple platforms

- MLX is for models that don't need to be fast

- Core AI is for models that run everywhere already and also need to be fast

This view is a bit off. First, keep in mind that MLX was and will not be able to access the ANE, so it's a total non-starter for anything user-facing. Based on updates to coreml docs, they're trying to sell CoreML as the tool for tabular or domain-specific applications and CoreAI for NNs moving forward.

> keep in mind that MLX was and will not be able to access the ANE

That's the rationale behind it not being fast.

> so it's a total non-starter for anything user-facing

Yep.

I use CoreML for models designed for other platforms. I port the models to it but it works for that without much trouble.

MLX is not for end user deployment.