AI Data Preparation & Feature Engineering › Data Augmentation

What are best practices for Data Augmentation

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Answer

When working with Data Augmentation, start with a clear goal, test on real data early, keep the approach as simple as possible at first, and follow established practices from the AI community rather than guessing.

Real-world example A team training an image model uses data augmentation to flip and rotate photos, effectively increasing the size of their training dataset.

Common follow-ups: What is AI Data Preparation & Feature Engineering, How is Data Augmentation evaluated in practice, What tools are commonly used for Data Augmentation

AI Data Preparation & Feature Engineering topics: Data Cleaning for AI Models Handling Missing Data Feature Scaling & Normalization