Model Deployment
Showing 36 of 36 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 cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
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 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.
A curated list of articles covering software engineering best practices for building production machine learning applications.
A lightweight header-only C++ library for running Keras (TensorFlow) models without linking against TensorFlow.
A collection of TensorFlow tutorials and examples covering image classification, GANs, text classification, and model deployment.
Fast multilayer perceptron neural network library for iOS and Mac OS X using Apple's Accelerate Framework.
A hands-on tutorial for training and deploying a machine learning model as a serverless REST API to predict cryptocurrency prices.
A universal model exchange and serialization format for decision tree forests, enabling cross-platform deployment.
A high-performance, concurrent distributed cache system built in Rust for low-latency, high-throughput workloads.
A tool to package, serve, and deploy any ML model on any platform using a GitOps approach.
Capture, analyze, and transform messy Jupyter notebooks into production data pipelines with just two lines of code.
A toolkit for developing and deploying TensorFlow Lite models on mobile and IoT devices with cross-platform support.
Deploy TensorFlow graphs for fast evaluation and export to environments without TensorFlow, using NumPy.
A Python framework for building and deploying machine learning APIs with a focus on simplicity and performance.
An open-source CAD framework for designing, simulating, and deploying deep neural networks on embedded platforms.
A community-driven collection of end-to-end tutorials for creating and deploying TensorFlow Lite models on mobile devices.
A GitHub template for automating machine learning workflows on Azure using GitHub Actions.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
A PMML evaluator library for Apache Spark that provides ML-compatible transformers for deploying predictive models.
A collection of refactored, high-quality Android examples demonstrating TensorFlow Lite for on-device machine learning tasks.
A ROS2 package that accelerates training and deployment of computer vision models for industrial applications.
Deploy and version machine learning models in Ruby applications using object storage like Amazon S3.
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