Building a Fully Automated Horse Racing AI Prediction Pipeline with Flutter + Supabase
The article describes building an automated AI prediction pipeline for horse racing data using Flutter and Supabase. It covers the architecture, data fetching, edge function implementation, and database optimization.
Why it matters
This project demonstrates how to build a robust, scalable, and automated AI prediction pipeline for a data-rich domain like horse racing using modern web technologies.
Key Points
- 1Leveraging rich, structured, and frequently updated horse racing data to build an AI prediction pipeline
- 2Using Python to fetch data from JRA (Japan Racing Association) and NAR (regional tracks) with EUC-JP encoding handling
- 3Implementing a tools-hub edge function to dispatch various horse racing actions like fetching today's races, making predictions, and calculating accuracy
- 4Optimizing database queries to handle large datasets and avoid timeouts
Details
The author built an automated AI prediction pipeline for horse racing data, covering both JRA (Japan Racing Association) and NAR (regional tracks) data. The pipeline fetches the data using a Python script, processes it through an edge function in tools-hub, and stores the results in a Supabase database. The edge function handles various actions like fetching today's races, making predictions, and calculating accuracy. To stay under the 50 Edge Function hard cap, all horse racing features are implemented as actions inside the tools-hub. The author also had to optimize database queries to handle large datasets and avoid timeouts when fetching all race results. The resulting system is a fully automated pipeline that powers a Flutter-based horse racing prediction web app.
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