Popular GitHub Repositories for Foods Delivery

?? CML - Continuous Machine Learning | CI/CD for ML

?️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

? A practical and imaginary food delivery microservices, built with .Net 8, MassTransit, Domain-Driven Design, CQRS, Vertical Slice Architecture, Event-Driven Architecture, and the latest technologies.

A data standard to enable right-of-way regulation, digital policy, geofencing, and two-way communication between mobility companies and public agencies worldwide.

Oinone is an enterprise-class productization engine that combines standardized R&D and agile delivery. Drawing from Odoo's engineering philosophy while significantly enhancing no-code capabilities, for both professional developers and citizen developers./Oinone是企业级产品化引擎，以低代码和无代码驱动的标准化研发与敏捷交付一体化研发框架。借鉴Odoo的工程化理念，并在无代码能力上显著增强，即面向专业研发，又能面向公民研发。

MLRun/Iguazio/Nuclio quality gate solution. The solution checks a quality of MLRun implementation/delivery.

Repo-level benchmark for real-world Code Agents: from repo understanding → env setup → incremental dev/bug-fixing → task delivery, with cost-aware α metric.

Oinone is an enterprise-class productization engine that combines standardized R&D and agile delivery. Drawing from Odoo's engineering philosophy while significantly enhancing no-code capabilities, for both professional developers and citizen developers./Oinone是企业级产品化引擎，以低代码和无代码驱动的标准化研发与敏捷交付一体化研发框架。借鉴Odoo的工程化理念，并在无代码能力上显著增强，即面向专业研发，又能面向公民研发。

A platform accelerating delivery of secure, trustworthy enterprise copilots.

Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.

An autonomous deep learning indoor delivery robot made with Jetson

The purpose of this project's design, development, and structure is to create an end-to-end Machine Learning Operations (MLOps) lifecycle to classify an individual's level of obesity based on their physical characteristics and eating habits.

This repo implements our paper, "Efficient Neural Neighborhood Search for Pickup and Delivery Problems", which has been accepted as short oral at IJCAI 2022.

Deep Reinforcement Learning applied to a modern 3D video-game environment called Delivery Duel.

A LangGraph-powered agent that finds and analyzes similar companies, leveraging Qdrant for data storage, Exa for research, and Gmail for report delivery. Performs comprehensive market analysis across 5 dimensions with 40+ comparison points per company.

Deployment template for a continuous training pipeline.

A project using machine learning to recommend new menu items based on foods the user already loves.

This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts. Ollama + LLM w/ functions + Natural language = User Interface for non-coders.

multi agent path finding and pickup and delivery (combined task allocation and path finding). work going on precendence constrained task allocation and path finding (PC-TAPF)