In addition to task-specific training APIs being available for many common model types, you can now define your own custom model and training pipelines by combining a rich set of ML building blocks with the new Create ML Components framework. Create ML frameworkĬreate ML is now available as a Swift framework on tvOS, along with iOS, iPadOS, and macOS. Preview your model’s predictions on live video from your iPhone camera. Explore key metrics and their connections to specific examples to help identify challenging use cases and further investments in data collection to help improve model quality. Interactively learn about your model’s accuracy in the new evaluation UI in the Create ML app. Support for sparse weight compression, restricting compute to the CPU and Neural Engine, and in-memory model instantiation are also now available. This, combined with APIs for supplying your own output buffer backings for predictions, enables more control of how efficiently data flows in and out of your Core ML models. The Core ML framework now supports Float16 input and output feature types. Aggregate timing data is summarized for each event, model, and submodel. Combine information from the Core ML, Neural Engine, and GPU Instruments to track when and where models are executed on accelerated hardware. Profile your app to view Core ML API calls and associated models using the Core ML template in Instruments. Review a summary of load and prediction times along with a breakdown of compute unit usage. Generate performance reports for Core ML models on your Mac or any connected iOS device without having to write any code. Use Xcode 14 to analyze and optimize your Core-ML-powered features.
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