Models bundled in apps can be updated with user data on-device, helping models stay relevant to user behavior without compromising privacy. Encrypt models NEW. Xcode supports model encryption enabling additional security for your machine learning models. Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation,.
Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple hardware and minimizing memory footprint and power consumption.
Run models fully on-device
Core ML models run strictly on the user’s device and remove any need for a network connection, keeping your app responsive and your users’ data private.
Run advanced neural networks
Core ML supports the latest models, such as cutting-edge neural networks designed to understand images, video, sound, and other rich media.
Deploy modelsNEW
With Core ML Model Deployment, you can easily distribute models to your app using CloudKit.
Convert models to Core ML
Models from libraries like TensorFlow or PyTorch can be converted to Core ML using Core ML Converters more easily than ever before.
Personalize models on-device
Models bundled in apps can be updated with user data on-device, helping models stay relevant to user behavior without compromising privacy.
Encrypt modelsNEW
Xcode supports model encryption enabling additional security for your machine learning models.
Create ML
Build and train Core ML models right on your Mac with no code.
Core ML Converters
Convert models from third-party training libraries into Core ML using the coremltools Python package.
Models
Get started with models from the research community that have been converted to Core ML.
Powerful Apple Silicon
Core ML is designed to seamlessly take advantage of powerful hardware technology including CPU, GPU, and Neural Engine, in the most efficient way in order to maximize performance while minimizing memory and power consumption.
The people working here in machine learning and AI are building amazing experiences into every Apple product, allowing millions to do what they never imagined. Because Apple fully integrates hardware and software across every device, these researchers and engineers collaborate more effectively to improve the user experience while protecting user data. Come make an impact with the products you create and the research you publish.
For Cecile, collaboration puts machine learning on the fast track.Cecile
Find a team and begin your own story here.
Machine Learning Infrastructure
Build the rock-solid foundation for some of Apple’s most innovative products. As part of this team, you’ll connect the world’s best researchers with the world’s best computing, storage, and analytics tools to take on the most challenging problems in machine learning. And this is Apple, so your team will innovate across the entire stack: hardware, software, algorithms — it’s all here. Areas of work include Back-End Engineering, Data Science, Platform Engineering, and Systems Engineering.
Deep Learning and Reinforcement Learning
Machine Learning Apps For Mac Free
Join a team of researchers and engineers with a proven track record in a variety of machine learning methods: supervised and unsupervised learning, generative models, temporal learning, multimodal input streams, deep reinforcement learning, inverse reinforcement learning, decision theory, and game theory. This team dives deep into deep learning and AI research to help solve real-world, large-scale problems. Areas of work include Deep Learning, Reinforcement Learning, and Research.
Natural Language Processing and Speech Technologies
This group is a collective of hands-on research scientists from a wide variety of fields related to natural language processing. Join them to work with natural language understanding, machine translation, named entity recognition, question answering, topic segmentation, and automatic speech recognition. This team’s research typically relies on very large quantities of data and innovative methods in deep learning to tackle user challenges around the world — in languages from around the world. Areas of work include Natural Language Engineering, Language Modeling, Text-to-Speech Software Engineering, Speech Frameworks Engineering, Data Science, and Research.
They call it machine learning, but Giulia keeps learning, too.Giulia
Machine Learning Apps For Mac Download
Computer Vision
Come solve the most challenging problems in computer vision and perception. Be part of a multidisciplinary team that designs algorithms to analyze and fuse complex sensor data streams. This team works on everything from low-level image processing algorithms to deep neural network approaches for object detection, always mindful of the balance between algorithm correctness and computational performance. Areas of work include Computer Vision, Data Science, and Deep Learning.
Applied Research
Transform groundbreaking ideas into revolutionary features. You’ll take part in core and applied machine learning research focused on both algorithm development and integration. As a software R&D engineer, you’ll develop cutting-edge machine learning algorithms to enable current and future Apple products and services in fields that include health, accessibility, and privacy. Areas of work include Machine Learning Platform Engineering, Systems Engineering, Data Science, and Applied Science.
Get discovered. Introduce yourself, and we’ll get in touch if there’s a role that seems like a good match.
Get started
Get started
Different together. At Apple, we’re not all the same. And that’s our greatest strength.
Learn more
Learn more