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.
Understanding Annotation
Annotation is the labor-intensive but essential process of attaching meaningful labels to raw data, creating the supervised learning datasets that power most modern AI applications. In computer vision, annotation involves drawing bounding boxes around objects, segmenting pixel regions, or tagging images with categories. In natural language processing, it includes labeling named entities, marking sentiment, or classifying document topics. The quality of annotations directly determines the ceiling of model performance, making careful guidelines and quality assurance critical. Large-scale annotation efforts often employ distributed teams through platforms like Amazon Mechanical Turk or specialized data labeling companies. The high cost of annotation has driven research into active learning, semi-supervised learning, and self-supervised techniques that reduce reliance on labeled data. As AI systems tackle more nuanced tasks, annotation increasingly requires domain expertise, such as radiologists labeling medical images or lawyers reviewing legal documents.
Category
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.
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.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features in a dataset while preserving its essential structure. Techniques like PCA and t-SNE help with visualization, noise reduction, and computational efficiency.