[{"data":1,"prerenderedAt":40},["ShallowReactive",2],{"project:/projects/limasense-mlops":3},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":9,"tags":10,"icon":16,"featured":17,"status":18,"role":19,"about":20,"stack":25,"body":27,"_type":34,"_id":35,"_source":36,"_file":37,"_stem":38,"_extension":39},"/projects/limasense-mlops","projects",false,"","LimaSense MLOps","Production ML backend for crop detection and quality grading",[11,12,13,14,15],"FastAPI","YOLO","Supabase","Docker","AWS App Runner","fastapi",true,"Production System","ML and Backend Engineer",[21,22,23,24],"Built a FastAPI service that serves computer-vision models for crop detection and crop-quality grading from submitted images.","Integrated YOLO inference for crop localization and A-D quality grading, with structured API responses for predictions, confidence scores, and detected regions.","Designed Supabase-backed workflows for users, scan history, reports, and stored images, with separate endpoints for user and administrative access.","Containerized the service with Docker and prepared AWS App Runner infrastructure for production deployment, including health checks and environment-based configuration.",[11,26,12,13,14,15],"Python",{"type":28,"children":29,"toc":30},"root",[],{"title":7,"searchDepth":31,"depth":32,"links":33},2,1,[],"markdown","content:projects:limasense-mlops.md","content","projects/limasense-mlops.md","projects/limasense-mlops","md",1786626763530]