Machine learning, a branch of artificial intelligence, involves using trained models to perform tasks such as identifying objects in images. Generative artificial intelligence (GenAI) creates new content, like a simple chat app that responds to user inputs with generated texts. Apple provides a workflow for developing apps using machine learning, which involves three main steps: training, preparing, and integrating models.
Apple’s Core ML has new features that simplify integrating AI models into apps. The recommended process for developing Apple Intelligence apps includes training, preparing, and integrating models. This method optimizes models, especially for mobile devices.
Training AI Models
The first step involves selecting a suitable base model and converting it into the required format if necessary. Platforms like Hugging Face offer a wide range of models, similar to GitHub for machine learning. Popular models such as Meta’s LLaMA, Microsoft’s BERT, and OpenAI’s GPT are available for download. Apple also shares tools and models on Hugging Face, though it only provides a paper for its Apple Foundation Model (AFM) on its Machine Learning Research page.
Foundation Models are ideal for apps as they are pre-trained for various tasks in language, audio, and video. These models can be fine-tuned for specific applications using Apple’s ML framework, MLX. This framework, shared as open-source on GitHub, is optimized for Apple Silicon and runs only on Apple’s processors. MLX’s functions are similar to popular frameworks like TensorFlow and PyTorch. It offers API bindings for C, C++, and Swift, as well as Python, which is favored in the AI community.
Preparing and Optimizing
Once a model is trained, the next step is preparation and optimization. This involves techniques like palletization, quantization, and pruning to ensure the model runs efficiently on mobile devices. These steps help in reducing the model size and improving performance without significantly affecting accuracy.
Integrating Models into Apps
The final step is integrating the trained and optimized models into the app. Apple’s tools and frameworks support this integration, making it easier to incorporate AI functionalities into applications. The integration process ensures the models work seamlessly with the app’s existing features, providing users with enhanced experiences.
In conclusion, Apple’s machine learning workflow offers a structured approach to developing AI-powered apps. By following the steps of training, preparing, and integrating, developers can create applications that leverage the power of AI effectively. This workflow is particularly beneficial for optimizing models for mobile devices, ensuring that apps run efficiently and provide valuable insights or functionalities to users.