How to prevent data leakage during feature engineering and train-test splitting in machine learning pipelines?

Here is the recommended approach for **How to prevent data leakage during feature engineering and train-test splitting in machine learning pipelines?**: 1. **Identify the Core Bottleneck**: Check if the bottleneck is caused by unindexed database queries, missing execution timeouts, or payload formatting issues. 2. **Implement Guardrails & Fallbacks**: Always add input validation at the boundary layer and set explicit timeouts on third-party service calls. ```bash # Verify system status php artisan --version ``` 3. **Keep Infrastructure Simple**: Avoid adding external infrastructure until your current framework setup (PostgreSQL, Redis, or job queues) hits clear limits. **Best Practice**: Monitor query response times and error rates continuously using APM tools to catch degradations early.