Automating Data CI/CD for Scalable MLOps Pipelines
Last Updated on September 30, 2025 by Editorial Team
Author(s): Kuriko Iwai
Originally published on Towards AI.
A step-by-step guide to achieving continuous data integration and delivery in production ML systems
Building robust Machine Learning (ML) applications demands meticulous version control for all components: code, models, and the data that powers them.
The article explores the complexities of establishing a Data CI/CD pipeline tailored for scalable MLOps, beginning with an in-depth overview of continuous integration and delivery for data. It discusses essential tools such as DVC (Data Version Control), Evidently for data drift detection, and Prefect for scheduling, detailing each component’s role in automating processes. The integration of these services ensures data quality, reproducibility, and reliability in machine learning workflows. Practical steps, code snippets, and the importance of real-time monitoring are emphasized, culminating in a strong call to action for using these frameworks to enhance model performance and maintain data integrity in production systems.
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