batch-inference

Batch inference scores millions of rows in a single run by reusing your service's feature engineering and model artifacts. It outputs partitioned Parquet files to avoid the latency overhead of real-time API calls.

Yes, you can schedule periodic batch predictions using a Kubernetes CronJob. The generated scaffold provides the necessary YAML to run nightly scoring or backfill historical data with built-in guardrails and observability.

Batch scoring maintains consistency by sharing the predict path between real-time and batch modes. This shared code path ensures both processes use the exact same feature engineering logic, reducing model drift.

The batch inference output uses partitioned Parquet files. Each record includes prediction_id, entity_id, model_version, prediction_score, and timestamp fields to ensure easy downstream data consumption and analysis.

Yes, Pandera validation is required to run batch inference. It validates the feature data before scoring, ensuring that the millions of rows processed during historical backfills meet the expected schema and quality standards.

The best way to backfill historical data is using a batch runner with a Kubernetes CronJob. This approach processes millions of rows per run, applying shared model artifacts to output partitioned Parquet files efficiently.