Batch inference applies a model to collected inputs on a schedule or on demand instead of answering each request continuously. It fits workloads where latency is mild but throughput, cost, and predictable completion matter: daily forecasts, large-scale indexing, or periodic reprocessing. section 15.5.2 section 15.5.1
A practical batch-inference design typically checks:
- Schedule jobs to read input data, run predictions, and write results to durable storage; expose an on-demand trigger when reprocessing is occasionally required. section 15.5.2 section 15.5.1
- Prioritize throughput and cost over per-request latency, then scale workers horizontally to handle large or uneven workloads. section 15.5.2 section 15.5.1
- Track job success, duration, failures, rows processed, resource use, data validation, and prediction quality rather than trusting a successful job status alone. section 14.3.1 section 15.5.1
- Make reruns safe and available: processing or source-data failures can be corrected by rerunning the model, unlike some high-load streaming failures. section 14.3.1
Photo indexing and demand forecasting
A photo-search system can generate vectors for its searchable catalog once a day, with an additional on-demand run after a core-model update. Its real-time query path remains separate, even when both paths use the same model converted to ONNX. Similarly, a retailer can run daily, weekly, or monthly forecasts from S3 and return predictions to S3 through scheduled AWS Batch jobs. section 15.5.2 section 15.5.1
Batching is not automatically a performance improvement. Profile the complete pipeline first: preprocessing, I/O, serialization, thread management, and caching may dominate model execution, leaving batching ineffective. section 15.4.1
