Training Data
Training data is the dataset used to teach a machine learning model to recognize patterns and make predictions. The quality, quantity, and representativeness of training data fundamentally determine model capabilities.
Understanding Training Data
Training data is the collection of examples used to teach a machine learning model the patterns and relationships it needs to make accurate predictions. The quality, quantity, diversity, and representativeness of training data fundamentally determine model performance, making dataset curation one of the most critical steps in any AI project. For supervised learning, training data includes input-output pairs with correct labels, while self-supervised learning derives training signals from the data itself. Issues like label noise, class imbalance, sampling bias, and data leakage from the validation set can severely degrade model reliability. Modern large language models train on trillions of tokens scraped from the internet, books, and code repositories, raising important questions about copyright, consent, and bias in AI. Synthetic data generation has emerged as a complementary strategy to address data scarcity and privacy concerns.
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Data Science
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A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.