Find and compare the best Artificial Intelligence software for Yandex Data Proc in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Yandex Data Proc on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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TensorFlow
TensorFlow
Free 2 RatingsTensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process. -
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scikit-image
scikit-image
Free 1 RatingScikit-image is an extensive suite of algorithms designed for image processing tasks. It is provided at no cost and without restrictions. Our commitment to quality is reflected in our peer-reviewed code, developed by a dedicated community of volunteers. This library offers a flexible array of image processing functionalities in Python. The development process is highly collaborative, with contributions from anyone interested in enhancing the library. Scikit-image strives to serve as the definitive library for scientific image analysis within the Python ecosystem. We focus on ease of use and straightforward installation to facilitate adoption. Moreover, we are judicious about incorporating new dependencies, sometimes removing existing ones or making them optional based on necessity. Each function in our API comes with comprehensive docstrings that clearly define expected inputs and outputs. Furthermore, arguments that share conceptual similarities are consistently named and positioned within function signatures. Our test coverage is nearly 100%, and every piece of code is scrutinized by at least two core developers prior to its integration into the library, ensuring robust quality control. Overall, scikit-image is committed to fostering a rich environment for scientific image analysis and ongoing community engagement. -
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Yandex DataSphere
Yandex.Cloud
$0.095437 per GBSelect the necessary configuration and resources for particular code segments in your ongoing project, as it only takes a few seconds to implement changes in a training scenario and secure the results. Opt for the appropriate setup for computational resources to initiate model training in mere seconds, allowing everything to be generated automatically without the hassle of infrastructure management. You can choose between serverless or dedicated operating modes, and efficiently manage project data, saving it to datasets while establishing connections to databases, object storage, or other repositories, all from a single interface. Collaborate with teammates globally to develop a machine learning model, share the project, and allocate budgets for teams throughout your organization. Launch your machine learning initiatives in minutes without requiring developer assistance, and conduct experiments that enable the simultaneous release of various model versions. This streamlined approach fosters innovation and enhances collaboration among team members, ensuring that everyone is on the same page.
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