Annual Auto-Retraining for NPB Baseball Predictions with GitHub Actions
📰 Dev.to · YMori
Learn how to implement annual auto-retraining for baseball predictions using GitHub Actions and CI/CD pipelines
Action Steps
- Build a CI/CD pipeline using GitHub Actions to automate model retraining
- Configure joblib to store model artifacts
- Create a FastAPI endpoint to serve metrics
- Test the pipeline to identify and fix bugs
- Apply annual metrics to update the model
- Compare model performance before and after retraining
Who Needs to Know This
Data scientists and software engineers can benefit from this article to improve their model deployment and maintenance workflows
Key Insight
💡 Implementing annual auto-retraining can improve model accuracy and maintain its performance over time
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🚀 Automate your model retraining with GitHub Actions and CI/CD pipelines! 💻
Key Takeaways
Learn how to implement annual auto-retraining for baseball predictions using GitHub Actions and CI/CD pipelines
Full Article
I added CI/CD to my NPB player performance prediction system: joblib model artifacts, annual metrics JSON, a FastAPI /metrics endpoint, and an 8-step GitHub Actions pipeline. Plus the 4 bugs that only showed up in CI.
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