Ml Framework
Showing 15 of 15 projects
An open source machine learning server for developers and data scientists, supporting event collection, algorithm deployment, and REST API queries.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A comprehensive Rust machine learning framework focused on preprocessing and classical algorithms, akin to scikit-learn.
A comprehensive .NET framework for machine learning, computer vision, statistics, and scientific computing.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A lambda architecture framework on Apache Spark and Kafka for building and deploying real-time large-scale machine learning applications.
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
A distributed streaming machine learning framework for mining big data streams with abstraction over processing engines.
A layer library for JAX-based machine learning projects, optimized for large-scale ML.
A Swift library for programmatically creating and exporting CoreML models using a Swift-native DSL and API.
A serverless machine learning framework that scales algorithms across cloud lambda functions.
A Ruby framework for text classification with interchangeable machine learning strategies.
A Python machine learning library with CPU and GPU support for tensor operations and model development.
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