Batch vs Real-Time Inference Explained¶
Introduction¶
Inference is how a model produces predictions after training. Batch inference scores many records on a schedule. Real-time inference returns predictions immediately through an API.
Why This Matters¶
The wrong inference mode creates unnecessary cost and complexity. A nightly churn score does not need a low-latency API; fraud checks during checkout usually do.
Core Concepts¶
Batch is scheduled and high-throughput. Real-time is request/response and needs availability, autoscaling, and latency monitoring. Near-real-time often uses queues or streams.
Practical Example¶
Batch scoring:
python batch_score.py --input s3://data/customers-2026-05-30.parquet --model models/churn.pkl --output s3://scores/churn-2026-05-30.parquet
Real-time request:
curl -s http://localhost:8000/predict -H "Content-Type: application/json" -d '{"tenure": 12, "monthly_charges": 89.9}'
{"churn_probability":0.73,"model_version":"churn:17"}
How This Fits in a Production Workflow¶
Batch often runs in Airflow, cron, Kubernetes Jobs, or data platforms. Real-time inference runs as a containerized API behind a service or ingress.
Common Mistakes¶
- Building an API for a monthly report.
- Running batch jobs without validating output row counts.
- Not logging model version in real-time responses.
- Ignoring retry behavior for queued inference.
Quick Checklist¶
- What latency is required?
- How many records are scored?
- Where does output go?
- How is failure retried?
- Is model version attached to predictions?
Related Guides¶
Summary¶
Compare batch scoring and real-time inference APIs with practical engineering examples.