Data Engineering
Synthetic Data
Fake but realistic data generated by AI to train other AI models, when real data is too expensive, sensitive, or scarce.
Definition
Artificially generated data that mimics the statistical properties of real-world data. Created using generative models, simulation, or rule-based systems. Used when real data is insufficient or too sensitive to use.
Why it matters
Solves the data scarcity problem for AI training while protecting privacy, especially valuable in healthcare and finance.
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 Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
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."
From vocabulary to outcomes
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