Building End-to-End Machine Learning (ML) Lineage for Serverless ML Systems
Last Updated on October 15, 2025 by Editorial Team
Author(s): Kuriko Iwai
Originally published on Towards AI.
A practical guide on ML lineage fundamentals and MLOps workflow implementation
Machine learning (ML) lineage is critical in any robust ML system to track data and model versions, ensuring reproducibility, auditability, and compliance.
This article discusses the importance of machine learning (ML) lineage in ensuring robust ML systems. It provides a detailed guide on integrating ML lineage into a project by outlining the full process from data extraction to model evaluation using serverless AWS Lambda, and emphasizes the significance of reproducibility and compliance in machine learning workflows, while explaining various stages such as data drift detection, preprocessing, model tuning, and fairness assessment.
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