Machine Learning Pipelines

Machine Learning Pipelines are systematic workflows that automate and orchestrate the end-to-end process of building, training, evaluating, and deploying machine learning models. They integrate data preprocessing, feature engineering, model training, validation, and deployment into a cohesive, reproducible sequence. This approach ensures consistency, scalability, and efficiency in ML projects by managing dependencies and versioning.

Also known as: ML Pipelines, ML Workflows, Data Science Pipelines, Model Pipelines, MLOps Pipelines

Why learn Machine Learning Pipelines?

Developers should learn and use Machine Learning Pipelines to streamline complex ML workflows, especially in production environments where reproducibility, automation, and collaboration are critical. They are essential for scenarios like continuous integration/continuous deployment (CI/CD) in ML, handling large datasets, and maintaining model performance over time with retraining and monitoring. This methodology reduces manual errors and accelerates experimentation cycles.

Learning Resources

• docs: Scikit-learn Pipeline Documentation
• tutorial: Kubeflow Pipelines Tutorial
• docs: MLOps with Azure Machine Learning Pipelines
• book: Building Machine Learning Pipelines - Book by Hannes Hapke and Catherine Nelson
• video: ML Pipelines with TensorFlow Extended (TFX) - YouTube Tutorial