ML Pipeline
Automated sequence of steps for data processing, feature engineering, training, evaluation, and deployment of an ML model.
ML pipelines automate the workflow from data processing through training to deployment – Kubeflow Pipelines and Apache Airflow are common orchestrators.
Explanation
ML pipelines orchestrate the entire ML workflow from raw data to production. They ensure reproducibility, automation, and scaling.
Marketing Relevance
ML pipelines are the foundation for professional MLOps and reproducible ML systems.
Common Pitfalls
Monolithic pipelines instead of modular steps. No idempotency. Missing error handling logic.
Origin & History
Scikit-learn popularized the pipeline concept for feature transformation. Apache Airflow (2014) brought DAG-based orchestration. Kubeflow Pipelines (2018) specialized this for ML on Kubernetes. Vertex AI Pipelines and SageMaker Pipelines followed.
Comparisons & Differences
ML Pipeline vs. Data Pipeline
Data pipelines process data (ETL); ML pipelines additionally include training, evaluation, and model deployment.
ML Pipeline vs. CI/CD Pipeline
CI/CD pipelines test and deploy code; ML pipelines orchestrate the entire ML lifecycle including data and models.