Model Deployment
Showing 16 of 16 projects
An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
Build and share machine learning web apps and demos in Python with minimal code.
A high-performance serving framework for large language models and multimodal models, delivering low-latency and high-throughput inference.
A high-performance neural network inference framework optimized for mobile platforms, enabling efficient AI deployment on edge devices.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
A blazing-fast, lightweight deep learning inference engine from Alibaba, optimized for on-device LLMs and Edge AI.
NVIDIA's SDK for high-performance deep learning inference optimization and deployment on NVIDIA GPUs.
A lightweight, modular, and scalable deep learning framework built on the original Caffe.
A Python library for online machine learning, designed for streaming data with a focus on user experience.
A comprehensive collection of machine learning tutorials and implementations in Python, covering algorithms from scratch to production deployment.
Transpile trained machine learning models into native code (Java, C, Python, Go, etc.) with zero dependencies.
Convert Caffe deep learning models to TensorFlow format for deployment and inference.
Instill Core is a full-stack AI infrastructure tool for data, model, and pipeline orchestration to build versatile AI-first applications.
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
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