ML Pipeline
An automated workflow that orchestrates the steps of training, evaluating, and deploying ML models.
Overview
An ML pipeline is an automated sequence of steps that takes raw data through processing, feature engineering, model training, evaluation, and deployment. Pipelines ensure reproducibility, reduce manual errors, and enable continuous training as new data becomes available.
Key Details
Pipeline orchestration tools include Apache Airflow, Kubeflow Pipelines, Prefect, and cloud-native solutions like AWS Step Functions or Vertex AI Pipelines. Each step is typically containerized and produces versioned artifacts. Well-designed pipelines include data validation, model validation gates, and automatic rollback capabilities for production deployments.