OmniML

OmniML

Software that helps you train a faster and smaller model, with compact neural architecture design targeting specific hardware, state-of-the-art training techniques, and compression. Learn more
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$10.0m

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Total Funding$10.0m

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OmniML.ai is a software company specializing in the development of efficient machine learning (ML) models. Their primary focus is on creating smaller and faster ML models through advanced techniques in neural architecture design, training, and compression. These models are optimized for specific hardware, ensuring high performance and efficiency.

The company serves a diverse range of clients, including businesses and developers who need optimized ML solutions for various applications. These applications include 2D and 3D detection, face pose recognition, Generative Adversarial Networks (GANs), and 2D and 3D segmentation. OmniML.ai operates in the rapidly growing artificial intelligence (AI) and machine learning market, which is seeing increasing demand for more efficient and effective ML models.

OmniML.ai's business model revolves around providing software solutions that enable clients to train and deploy ML models that are both compact and high-performing. They offer a "push the button" solution for hardware-aware model design, leveraging state-of-the-art AutoML (Automated Machine Learning) techniques. This means that clients can easily create optimized models without needing deep expertise in ML.

The company makes money by selling its software solutions and services to clients who need to improve the performance and efficiency of their ML models. Their offerings include fast inference engines that are optimized for mobile devices and various hardware platforms like STM32, Raspberry Pi, and Jetson devices. These engines are reported to be three times faster than TensorFlow Lite Micro, a popular ML framework.

In summary, OmniML.ai provides cutting-edge software solutions for creating smaller, faster, and more efficient ML models, catering to a wide range of applications and hardware platforms.

Keywords: Efficient ML Models, Neural Architecture, Model Compression, AutoML, Inference Engines, 2D Detection, 3D Detection, Face Recognition, GANs, Segmentation.