Batching processes a collected group of examples or requests together instead of updating or serving each one continuously. In model serving, it primarily improves throughput, but the right design depends on the allowed latency and workload shape. section 15.2.2 section 15.2.1
Choose the batching pattern against the product and system constraints:
- Use offline batches when predictions can wait; use dynamic batching when requests arrive online and a short accumulation window remains acceptable. section 15.2.2 section 15.5.1
- Set a time or request-count limit, such as waiting 20 milliseconds or 32 requests, whichever comes first, to balance responsiveness and throughput. section 15.2.2
- Group variable-length language-model inputs by size to reduce padding overhead, accepting smaller batches or a longer accumulation window. section 15.2.2
- Treat batch throughput, latency, cost, scaling, data security, and prediction quality as operational requirements rather than implementation details. section 15.5.1 section 15.2.1
Retail demand forecasting
A retailer can run daily, weekly, and monthly forecasts over large datasets using scheduled containers, horizontally scaled batch jobs, and results written back to storage. Monitoring tracks job success, duration, failures, processed rows, resource use, accuracy, and data validation. section 15.5.1
Batch learning also supports scheduled model updates: retraining on the latest data changes the model, so the resulting behavior must be checked against the reason for retraining and the expected output quality. section 14.1.2
