Data Engineering
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."
Definition
The process of attaching meaningful tags, categories, or annotations to raw data so it can be used for supervised machine learning. Can be done by humans, automated tools, or a combination.
Why it matters
The quality of labels directly determines the quality of the model, garbage in, garbage out.
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 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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