The framework provides software developers with powerful deep learning and traditional machine learning algorithms for creating applications that fuel digital transformation

ABBYY, a Digital Intelligence company, announced the launch of NeoML, an open-source library for building, training, and deploying machine learning models. Available now on GitHub, NeoML supports both deep learning and traditional machine learning algorithms. The cross-platform framework is optimized for applications that run in cloud environments, on desktop and mobile devices. Compared to a popular open-source library, NeoML offers 15-20% faster performance for pre-trained image processing models running on any device.[1] The combination of higher inference speed with platform-independence makes the library ideal for mobile solutions that require both seamless customer experience and on-device data processing.

As open source becomes a staple in the development of mission-critical software, with 95% of IT leaders asserting that it is strategically important, ABBYY aims to support advancements in artificial intelligence by open-sourcing its machine learning framework. Developers can use NeoML to build, train, and deploy models for object identification, classification, semantic segmentation, verification, and predictive modeling, in order to achieve various business goals. For instance, banks can develop models to manage credit risk and predict customer churn, telecom companies – to analyze the performance of marketing campaigns, retail and fast-moving consumer goods (FMCG) – to build remote client identification with face recognition and data verification. One of the key advantages of the framework is its dramatically more efficient use of available cloud resources.

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NeoML is designed as a universal tool to process and analyze data in a variety of formats including text, image, video, and others. It supports​​C++, Java, and Objective-C programming languages; Python will be added shortly. NeoML’s neural network models support over 100 layer types. It also offers 20+ traditional ML algorithms such as classification, regression, and clustering frameworks. The library is fully cross-platform – a single code base that can be run on all popular operating systems including Windows, Linux, macOS, iOS, and Android – and optimized for both CPU and GPU processors.

“The launch of NeoML reflects our commitment to contribute to industry-wide AI innovation,” said Ivan Yamshchikov, AI Evangelist at ABBYY. “ABBYY has a proven track record of technological innovation with over 400 patents and patent applications. Sharing our framework allows developers to leverage its inference speed, cross-platform capabilities, and especially its potential on mobile devices, while their feedback and contribution will grow and improve the library. We are thrilled to promote advancements in AI and support machine learning being applied to increasingly high-value and impactful use cases.”

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NeoML supports the Open Neural Network Exchange (ONNX), a global open ecosystem for interoperable ML models, which improves compatibility of tools making it easier for developers to use the right combinations to achieve their goals. The ONNX standard is supported jointly by Microsoft, Facebook, and other partners as an open source project.

ABBYY invites developers, data scientists, and business analysts to use and contribute to NeoML on GitHub, where its code is licensed under the Apache License 2.0. The company offers personalized developer support, ongoing review of reports, regular updates, and performance enhancements. Going forward, ABBYY plans to add new algorithms and architectures, as well as further increase the speeds achievable using the framework algorithms.

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