OEMad
OEMad.ai is a machine learning-powered OEM ad platform offering direct access to millions of users across Xiaomi, Transsion, Oppo, VIVO, Huawei, and Samsung devices worldwide.
Built with event-based optimization in mind, OEMad enables advertisers to scale campaigns based on real in-app actions such as registrations, purchases, and beyond — with full transparency and no intermediaries.
OEMad is your gateway to the OEM advertising ecosystem.
No brokers. No workarounds. No “waiting for a reply from China.”
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Huawei Cloud ModelArts
ModelArts is a comprehensive AI development platform provided by Huawei Cloud, designed to streamline the entire AI workflow for developers and data scientists. It offers a full-lifecycle toolchain that includes data preprocessing, semi-automated data labeling, distributed training, automated model building, and flexible deployment options across cloud, edge, and on-premises environments. It supports popular open source AI frameworks such as TensorFlow, PyTorch, and MindSpore, and allows for the integration of custom algorithms tailored to specific needs. ModelArts features an end-to-end development pipeline that enhances collaboration across DataOps, MLOps, and DevOps, boosting development efficiency by up to 50%. It provides cost-effective AI computing resources with diverse specifications, enabling large-scale distributed training and inference acceleration.
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Ring
The Ring is a practical general-purpose multi-paradigm language. The supported programming paradigms are imperative, procedural, object-oriented, declarative using nested structures, functional, meta programming and natural programming. The language is portable (Windows, Linux, macOS, Android, WebAssembly, etc.) and can be used to create Console, GUI, Web, Games and Mobile applications. The language is designed to be simple, small and flexible. The language is simple, trying to be natural, encourage organization and comes with transparent and visual implementation. It comes with compact syntax and a group of features that enable the programmer to create natural interfaces and declarative domain-specific languages in a fraction of time. It is very small, flexible and comes with smart garbage collector that puts the memory under the programmer control. It supports many programming paradigms, comes with useful and practical libraries.
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Caffe
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.
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