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Model Deployment · Awesome GitHub Repositories

8 repos

Awesome GitHub RepositoriesModel Deployment

Infrastructure and tools required to package, serve, and execute machine learning models in production environments.

Explore 8 awesome GitHub repositories matching artificial intelligence & ml · Model Deployment. Refine with filters or upvote what's useful.

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Awesome Model Deployment GitHub Repositories

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  • tensorflow/tensorflow

    tensorflow/tensorflow

    193,864GitHubView on GitHub↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The syst

    C++deep-learningdeep-neural-networksdistributed
  • mlabonne/llm-course

    mlabonne/llm-course

    75,340GitHubView on GitHub↗

    This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as we

    courselarge-language-modelsllm
  • PaddlePaddle/PaddleOCR

    PaddlePaddle/PaddleOCR

    70,931GitHubView on GitHub↗

    PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into indepen

    Pythonai4sciencechineseocrdocument-parsing
  • vllm-project/vllm

    vllm-project/vllm

    70,745GitHubView on GitHub↗

    vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token gen

    Pythonamdblackwellcuda
  • hiyouga/LlamaFactory

    hiyouga/LlamaFactory

    67,386GitHubView on GitHub↗

    LlamaFactory is a unified framework for fine-tuning and adapting large language models. It provides a comprehensive platform that standardizes training workflows across diverse machine learning architectures, allowing users to execute both full-tuning and parameter-efficient methods through a single interface. The pro

    Pythonagentaideepseek
  • facebookresearch/segment-anything

    facebookresearch/segment-anything

    53,431GitHubView on GitHub↗

    This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring serve

    Jupyter Notebook
  • ultralytics/ultralytics

    ultralytics/ultralytics

    53,426GitHubView on GitHub↗

    Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification

    Pythonclicomputer-visiondeep-learning
  • tensorflow/tfjs-examples

    tensorflow/tfjs-examples

    6,783GitHubView on GitHub↗

    This repository provides a collection of practical demonstrations and implementation guides for machine learning tasks using TensorFlow.js. It serves as a resource for developers to explore model architectures, training workflows, and data manipulation techniques across domains such as computer vision, natural language

    JavaScript

Explore sub-tags

  • Distributed Model ServersServices that expose generative model capabilities over network protocols for integration into external applications.
  • High-Throughput Model ServingArchitectures designed to handle large volumes of concurrent inference requests with low latency.
  • Inference Optimization TechniquesMethods to improve the speed, latency, and resource efficiency of model inference.
  • Inference ServersServices that provide standardized API endpoints for model execution.
  • LLM Serving ArchitecturesHigh-performance systems and engineering architectures designed to deploy and serve large language models at scale.
  • Local Model Inference ServersComponents that host models locally to provide low-latency predictions via standard network APIs.
  • Model Execution APIsInterfaces for loading and running pre-trained model assets.
  • Model Export PipelinesSystems that convert trained model weights into various standardized formats for cross-platform compatibility.
  • Model ExportersUtilities that convert machine learning models into standardized formats for cross-platform inference.
  • Model Inference APIsStandardized interfaces for serving model predictions via local or remote endpoints.
  • ONNX Model ExportersConverting machine learning models into the standardized ONNX format.
  • ONNX Model ExportsThe conversion of trained models into the Open Neural Network Exchange format for cross-engine compatibility.
  • Online Model ServersServices that provide real-time model inference and chat completions via standard API protocols.