Popular GitHub Repositories for Interoperability

Open standard for machine learning interoperability

An open protocol enabling communication and interoperability between opaque agentic applications.

Library for Semi-Automated Data Science

A small neural network to provide interoperability between the latents generated by the different Stable Diffusion models.

Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.

EDUMCP is a protocol that integrates the Model Context Protocol (MCP) with applications in the education field, dedicated to achieving seamless interconnection and interoperability among different AI models, educational applications, smart hardware, and teaching AGENTs.

A library for directly calling PyTorch ML models from Fortran.

TengineGst is a streaming media analytics framework, based on GStreamer multimedia framework, for creating varied complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Tengine Toolkit Inference Engine backend, across varied architecture - CPU, iGPU and VPU.

Healthcare OS for interoperability and AI-driven data processing

Samples for NimbusML, a Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.

Generalized API for browser-based generative art projects, plug & play support for platform specifics, parameter declarations, GUI creation, IPC

Easy to use Verifiable AI and smart contracts interoperability.

This repository shows an example of how to use the ONNX standard to interoperate between different frameworks. In this example, we train a model with PyTorch and make predictions with Tensorflow, ONNX Runtime, and Caffe2.

Export MXNet model to ONNX and use it in ML.NET

Welcome to Healthcare Thoughts! This is where I share my insights at the intersection of healthcare and technology. Explore articles on federated learning, blockchain, AI in medicine, health data privacy, and telemedicine. Let's engage in meaningful conversations about the exciting world of healthcare tech!

Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools.

A high-performance microservices metacognitive orchestration framework built in Rust

Standard format for describing tools functionally, enabling smarter, multilingual AI agents.Standard format for describing tools functionally, enabling smarter, multilingual AI agents.

This project provides fundamental dataset operations in C and enables interoperability with Python through Cython compilation.