{"data":{"full_name":"PierreExeter/MLOps-Pipeline-Deployment","name":"MLOps-Pipeline-Deployment","description":"A deployed machine learning model that predicts patient medical charges based on demographic and health data. Features a Flask API, a web front-end, and is containerized with Docker for deployment on Azure.","stars":0.0,"forks":0.0,"language":"CSS","license":"GPL-3.0","archived":0.0,"subcategory":"mlops-workflow-orchestration","last_pushed_at":"2025-10-24T10:04:09+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":6.0,"adoption_score":0.0,"maturity_score":9.0,"community_score":0.0,"quality_score":15.0,"quality_tier":"experimental","risk_flags":"['no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-08T11:16:59.667901+00:00"}}