NeoML: Open source library for building, training, and deploying machine learning models

ABBYY launched 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. 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.

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 efficient use of available cloud resources.

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.”

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.

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