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What is Inference Pipelines

Definition

An inference pipeline is the end-to-end sequence that turns a raw request into a model prediction in production, handling preprocessing, feature retrieval, model execution, and post-processing so that serving a prediction is reliable, consistent, and maintainable.

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Standardises the steps from raw input to served prediction
Ensures preprocessing matches training, avoiding skew
Makes production serving reliable, monitorable, and maintainable
Supports scaling and versioning of the prediction path

Real World Example

A recommendation service runs an inference pipeline that validates the request, fetches user features from the feature store, runs the model, and formats results, ensuring every prediction follows the same reliable, consistent path.

FAQs

What stages does an inference pipeline include?

Typically input validation, preprocessing, feature retrieval, model prediction, and post-processing of the output.

Why use a defined inference pipeline?

It ensures consistent, reliable, monitorable serving and keeps production preprocessing aligned with training.

How does it relate to training pipelines?

Inference pipelines must mirror the training pipeline's preprocessing to avoid skew, but they run in real time on single or batched requests.