data caching

Data caching is discussed in several sessions at WWDC 2024, particularly in the context of SwiftData and machine learning models.

1. SwiftData: In the session Create a custom data store with SwiftData, the Datastore protocol is highlighted as it defines the functionality needed for a store to be usable by the model context, including caching. This allows for efficient data retrieval and persistence, supporting features like migration, history tracking, and CloudKit sync.

2. Machine Learning with Core ML: The session Deploy machine learning and AI models on-device with Core ML discusses the use of a key-value (KV) cache to store key and value vectors for language models. This caching mechanism helps reduce overhead and improve inference efficiency by avoiding the recomputation of vectors, thus speeding up prediction times.

3. Machine Learning with Metal: In the session Accelerate machine learning with Metal, the use of a KV cache is also explored. The session explains how to update the cache in place using operations like sliceupdate, which optimizes memory usage and improves performance.

These sessions provide insights into how caching is implemented and optimized in different contexts, from data persistence in SwiftData to performance improvements in machine learning models.