Fully Automating a Horse Racing AI Prediction Pipeline with Flutter & Supabase

product #pipeline 📝 Blog|Analyzed: Apr 15, 2026 22:38•

Published: Apr 15, 2026 16:45

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1 min read

Analysis

This project is a brilliant showcase of how to build an end-to-end, fully automated machine learning pipeline using modern, accessible tools. By combining Flutter, Supabase Edge Functions, and GitHub Actions, the developer created a robust system capable of handling complex data ingestion for both JRA and NAR racing. It is a highly inspiring example of practical AI application development and seamless integration!

Key Takeaways

•The pipeline autonomously fetches data from both major JRA and 15 regional NAR tracks using Python.
•Supabase Edge Functions handle prediction logic and data routing while strictly respecting the 50-function hard cap.
•GitHub Actions are scheduled to automatically execute the data collection and prediction pipeline every hour.

Reference / Citation

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"Starting from the idea that "it would be interesting to predict horse racing with AI", I built a fully automated pipeline connecting Flutter UI, Supabase Edge Functions, and GitHub Actions."

Qiita MLApr 15, 2026 16:45

* Cited for critical analysis under Article 32.

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