Batch Learning
A machine learning approach where the model is trained on the entire dataset at once, as opposed to incremental learning.
Definition
A training paradigm in which all available data is processed in fixed-size batches during each epoch. Batch learning contrasts with online or incremental methods and is suited for stable datasets. It requires retraining from scratch when new data arrives, which can be resource-intensive. Governance must schedule retraining cycles and manage associated compute and data-versioning costs.
Real-World Example
A retail analytics team uses batch learning to retrain its demand-forecasting model every Sunday: it processes the entire week’s sales data in nightly batches, recalibrates the model, and deploys the updated version before Monday’s operational planning meeting.
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