Analysis
This is a fantastic showcase of modern serverless architecture and practical machine learning integration! By seamlessly connecting Flutter, Supabase, and GitHub Actions, the developer created a robust, fully automated prediction pipeline. The comprehensive support for both JRA and NAR racing data, enriched with historical performance metrics, makes this an incredibly detailed and exciting project.
Key Takeaways
- •The system features a fully automated pipeline that fetches data and runs predictions hourly via GitHub Actions.
- •It supports a massive array of data points across 15 NAR tracks and JRA, including previous race stats, horse weight, and age.
- •The developer cleverly utilized a unified Supabase Edge Function with action branching to easily stay under the platform's 50-function hard cap.
Reference / Citation
View Original"[JRA/NAR データ取得] → fetch_horse_racing.py (Python) ↓ [tools-hub EF] → horseracing.today / predict_all / predictions / accuracy ↓ [Supabase DB] → horse_races / horse_results テーブル ↓ [horse-racing-update.yml] → 1時間毎に自動実行 (GitHub Actions) ↓ [Flutter UI] → horse_racing_predictor_page.dart (3タブ構成)"