Data Sampling
The technique of selecting a representative subset from a larger dataset for analysis or model training, reducing computational cost while preserving the statistical properties of the full dataset.
Sampling enables work with datasets too large to process in full. Random sampling selects records with equal probability. Stratified sampling ensures proportional representation of important subgroups (maintaining the same class ratio in a classification dataset). Reservoir sampling handles streaming data where the total size is unknown. Importance sampling weights samples by their relevance to the target distribution.
The key consideration is sample size. Too small a sample introduces high variance and may miss rare but important patterns. Too large a sample wastes computation without meaningfully improving results. Statistical power analysis helps determine the minimum sample size needed for a given confidence level and effect size.
For AI teams, sampling strategies directly impact model quality. Downsampling majority classes addresses class imbalance. Stratified sampling ensures rare categories are represented in evaluation sets. Progressive sampling starts with small datasets for rapid prototyping and scales up for final training. Understanding sampling theory prevents common mistakes like evaluating model performance on a biased subsample or training on a sample that does not represent the production data distribution.
Related Terms
Cosine Similarity
A measure of similarity between two vectors based on the cosine of the angle between them, ranging from -1 (opposite) to 1 (identical), commonly used to compare embeddings.
Dimensionality Reduction
Techniques that reduce the number of dimensions in high-dimensional data while preserving meaningful structure, used for visualization, compression, and noise removal.
Batch Inference
Processing multiple ML predictions as a group at scheduled intervals rather than one-at-a-time on demand, optimizing for throughput and cost over latency.
Real-Time Inference
Generating ML predictions on-demand as requests arrive, typically with latency requirements under 200ms for user-facing features.
Data Pipeline
An automated sequence of data processing steps that moves data from source systems through transformations to destination systems, enabling reliable and repeatable data flows across an organization.
ETL (Extract, Transform, Load)
A data integration pattern that extracts data from source systems, transforms it into a structured format suitable for analysis, and loads it into a target data warehouse or database.