AI Data Preparation & Feature Engineering › Data Labeling & Annotation

What are best practices for Data Labeling & Annotation

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Answer

When working with Data Labeling & Annotation, 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 hires human annotators to label thousands of medical images as showing a tumor or not before training a diagnostic model.

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

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