Stages of an end-to-end ML pipeline

Why it exists

Manually shepherding data to a deployable model is slow and error-prone. An automated end-to-end pipeline makes the path from new data to a release-ready model repeatable, testable, and triggerable, which is the core of MLOps continuous training.

The stages

Data ingestion pulls and lands new raw data from sources into the pipeline. Data validation checks schema, types, ranges, and missing values, and verifies the new batch matches expectations, halting on anomalies. Data preprocessing and feature engineering clean, transform, and compute features, ideally using shared definitions so training and serving match. Model training fits the model on the prepared data, often with hyperparameter tuning. Model evaluation scores the candidate on a held-out set and compares it against the current production model and predefined thresholds; only a model that wins advances. Model registration packages the validated model with its metadata, metrics, and lineage into a model registry, producing a versioned, deployment-ready artifact. Deployment and monitoring then follow downstream.

Gates between stages

The two critical gates are data validation, which stops corrupt or drifted data from poisoning training, and model evaluation, which stops a worse model from being promoted. These gates are what make the automation safe.

When it matters

It matters for any model that retrains regularly or must respond to fresh data, where manual steps would not keep up.

A concrete example

New daily transactions arrive; ingestion lands them, validation rejects a batch missing a required column, and once fixed, features are computed, a model is trained, evaluation shows it beats the live model's recall, and it is registered as version forty-two in the registry, ready for the deployment stage to pick up.