ML Pipelines

ML Pipelines automate the end-to-end flow of data ingestion, preprocessing, model training, and deployment to ensure reproducible and scalable machine learning workflows.

In production machine learning, manual workflows fail at scale. ML Pipelines solve this by structuring the entire lifecycle (from raw data extraction and feature engineering to model training, evaluation, and deployment) into a single, automated DAG (Directed Acyclic Graph). By orchestrating these steps with tools like Kubeflow, Apache Airflow, or TFX, engineering teams eliminate training-serving skew, ensure strict reproducibility, and automate continuous retraining when production data drifts. It is the operational backbone that transforms isolated notebooks into reliable, self-healing AI systems.

https://developers.google.com/machine-learning/guides/pipelines