Data Engineering
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Definition
Techniques for artificially increasing the size and diversity of a training dataset by applying transformations to existing data. Common in computer vision (rotation, flipping) and NLP (paraphrasing, back-translation).
Why it matters
Improves model robustness and performance when real-world labeled data is expensive or limited.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
Data Pipeline
The automated plumbing that moves data from where it's collected to where it's analyzed and used.
From vocabulary to outcomes
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