Pipeline
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
An ML pipeline chains data preparation, model training, evaluation, deployment, and monitoring into one automated workflow that can be rerun end to end.
What is a Pipeline?
A machine learning pipeline is an automated sequence of steps that turns raw data into a deployed, monitored model: prepare the data, train, evaluate, deploy, monitor. Each step feeds the next, and the whole chain can be rerun from the top.
The point is repeatability. A model built by hand in a notebook depends on whatever its author did that day; a pipeline produces the same model from the same data every time, and leaves a record of how.
How a Pipeline Works
A typical pipeline runs five stages in order.
- Data preparation: raw data is collected, cleaned, and transformed into training features.
- Training: the pipeline fits a candidate model on the prepared data (model training).
- Evaluation: the candidate is scored on held-out data and compared against the version already in production.
- Deployment: if the candidate wins, the pipeline promotes it to production (deploy).
- Monitoring: the live model is watched for failures and data drift, which eventually triggers the pipeline again.
Because every stage is scripted, the pipeline runs on a schedule or on a trigger – new data arrives, drift is detected – without a person repeating the manual steps.
Example of a Pipeline
An e-commerce company retrains its product recommendation model weekly. Every Sunday night the pipeline pulls the week’s purchase logs, rebuilds the features, and trains a new candidate.
The evaluation stage compares the candidate to the live model on the most recent held-out week. If the candidate wins, it is registered in the model registry and deployed automatically; if it loses, the pipeline stops and flags the run for review. No engineer touches the process unless something breaks.
Related AI terms: Deploy · Model Registry · MLflow · Model Training · Data Drift
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About