CoreML이란
Generated on 8/20/2024
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Core ML is a framework provided by Apple that allows developers to deploy machine learning models on Apple devices, such as iOS, iPadOS, and macOS. It is designed to optimize the performance of AI models by automatically distributing workloads across the CPU, GPU, and neural engine, maximizing hardware utilization. Core ML simplifies the development workflow with Xcode integration and provides tools for model optimization, including techniques like quantization and efficient key-value caching for large language models.
Core ML supports a wide range of AI models, including those developed with PyTorch, and can convert them into the Core ML format for deployment. It also includes new features like ML tensor types for simplifying model integration and multifunction models for deploying models with multiple functionalities. Additionally, Core ML offers performance tools to help developers profile and debug their models.
For more detailed information, you can refer to the session Explore machine learning on Apple platforms (07:16) which covers running models on devices.
Platforms State of the Union
Discover the newest advancements on Apple platforms.
Explore machine learning on Apple platforms
Get started with an overview of machine learning frameworks on Apple platforms. Whether you’re implementing your first ML model, or an ML expert, we’ll offer guidance to help you select the right framework for your app’s needs.
Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.
What’s new in Create ML
Explore updates to Create ML, including interactive data source previews and a new template for building object tracking models for visionOS apps. We’ll also cover important framework improvements, including new time-series forecasting and classification APIs.