{"repo":"hoangsonww/YouTube-Success-Prediction-ML","free":true,"listed":false,"github":"https://github.com/hoangsonww/YouTube-Success-Prediction-ML","clone":"git clone https://github.com/hoangsonww/YouTube-Success-Prediction-ML.git","description":"📀 A production-oriented AI/machine learning platform that predicts YouTube channel performance while also providing analytics, clustering intelligence, and full MLOps + cloud deployment workflows.","language":"TypeScript","stars":19,"topics":["argo-rollouts","argocd","aws","azure","data-processing","data-visualization","deep-learning","docker","gcp","jenkins"],"license":"MIT","category":"machine-learning","readme_excerpt":"YouTube Success Prediction ML Platform This repository contains a production-oriented machine learning platform for YouTube channel success prediction and intelligence. The system combines: 1. Supervised prediction of channel outcomes. 2. Unsupervised channel archetype discovery. 3. Global analytics and map-ready country/category intelligence. 4. Production API and frontend delivery with MLOps artifacts. 5. Multi-cloud deployment and GitOps strategy. 6. Comprehensive documentation and operational runbooks. 7. Quality gates, testing, and formatting for maintainability. 8. Detailed design and architecture documentation for engineering alignment. This README.md is only the operational entrypoint. For detailed design and subsystem contracts, use the linked documentation map below. Table Of Contents - Document Metadata - Documentation Map - Project Overview - Dataset Overview - Implemented Capabilities - Technology Stack - Repository Layout - Quick Start - Environment Configuration - End-To-End Pipeline Execution - API Reference - Frontend Reference - MLOps And Governance - MLOps Extension Runtime Controls - Deployment - Code Style And Formatting - Quality Gates And Testing - Operations Runbook - Troubleshooting - Detailed Design - Documentation Governance - Documentation Architecture - Production Maturity Checklist Document Metadata This document serves as the operational and product engineering entrypoint for the YouTube Success Prediction ML Platform. It provides a high-level o","default_branch":null,"files":null,"tree":[],"storefront":"/r/hoangsonww","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/hoangsonww/YouTube-Success-Prediction-ML/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}