# Batch Inference

Batch inference in Valohai runs as a standard execution, letting you process datasets or file collections at scale without managing infrastructure.

### How it works

Batch inference uses the same execution system you use for training:

1. Define a step in `valohai.yaml` with your inference code
2. Specify inputs (model files and data to process)
3. Run the execution via CLI, API, or schedule it
4. Collect results from outputs

**Key advantage:** You already know this system. If you've run training jobs, you can run inference jobs.

### What you can do

* Process thousands of images, CSVs, or other file types
* Schedule recurring inference jobs (e.g., nightly predictions)
* Trigger inference via API when new data arrives
* Chain inference into pipelines after training completes
* Track inference metrics alongside training metrics

### Example use cases

**Image classification at scale** Process a directory of product images to tag inventory items.

**Batch predictions on tabular data** Run monthly churn predictions on your entire customer database.

**Document processing** Extract entities from legal documents or medical records in batches.

### When to use batch inference

Choose batch inference when:

* You're processing datasets, not individual requests
* Latency requirements are in minutes or hours, not milliseconds
* You want to leverage Valohai's execution tracking and versioning
* You need to schedule or automate inference runs

**Need lower latency?** Check out [Real-Time Endpoints](https://github.com/valohai/dokuhai/blob/main/real-time/concepts.md) for sub-second predictions.

### Next steps

See practical examples:

* [CSV Inference Example](https://github.com/valohai/dokuhai/blob/main/docs/csv-inference.md)
* [Image Inference Example](https://github.com/valohai/dokuhai/blob/main/docs/image-inference.md)

Or jump straight to defining your inference step in `valohai.yaml`.


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