Why Neural Networks Fail Even After High Accuracy

1. 1Evaluate your model's performance on a held-out test set to check for overfitting
2. 2Investigate data drift and concept drift to ensure the model is generalizing well
3. 3Test your model on diverse data to identify potential biases
4. 4Monitor your model's performance in production to detect any deviations from expected behavior
5. 5Re-train your model on new data to adapt to changing conditions

Data scientists and machine learning engineers can benefit from understanding the pitfalls of high-accuracy models to improve their deployment strategies

💡 High accuracy on a test set does not guarantee success in production due to issues like overfitting, data drift, and bias

🚨 High accuracy ≠ production readiness 🚨