what is createml
Generated on 3/21/2025
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Create ML is a tool within Apple's ecosystem designed to help developers build and deploy machine learning models in their apps. It consists of the CreateML app and underlying frameworks, allowing users to train models with ease. The CreateML app provides templates for various tasks such as image classification, object detection, and more, making it accessible for those new to machine learning. It leverages system domain frameworks like Vision, Natural Language, and Sound Analysis to customize models with your training data. The output is a model that can be deployed into your app.
Recent updates to Create ML include enhancements to the app, a new object tracking feature for spatial computing experiences, and new components for time series forecasting and classification. These updates aim to simplify the process of integrating machine learning into apps across Apple's operating systems.
For more detailed information, you can refer to the session What’s new in Create ML (00:00:51).
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.
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.
Bring your machine learning and AI models to Apple silicon
Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.
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.
Support real-time ML inference on the CPU
Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.
Platforms State of the Union
Discover the newest advancements on Apple platforms.
Explore object tracking for visionOS
Find out how you can use object tracking to turn real-world objects into virtual anchors in your visionOS app. Learn how you can build spatial experiences with object tracking from start to finish. Find out how to create a reference object using machine learning in Create ML and attach content relative to your target object in Reality Composer Pro, RealityKit or ARKit APIs.