Data Engineering
Training Data
The examples an AI learns from, the quality and diversity of this data determines everything about the model's capabilities.
Definition
The dataset used to train a machine learning model. Includes input features and (for supervised learning) target labels. Quality, diversity, and representativeness directly impact model performance.
Why it matters
The single most important factor in model quality, a great algorithm on bad data will always lose to a simple algorithm on great data.
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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