DataOps: Orchestrating Reliable ML Data Pipelines

Abstract

The proliferation of Machine Learning (ML) models in production has elevated the criticality of managing data reliably throughout the ML lifecycle. DataOps has emerged as a disciplined practice combining Agile, DevOps, Lean, and statistical process control to enhance data pipeline reliability, speed, and governance. Originally coined in 2014 and gaining traction by 2017–2018, DataOps promotes automation, collaboration, monitoring, and versioning of data workflows across teams Wikipediadevopsschool.com. This paper presents an in-depth analysis of pre-2020 DataOps practices applied to ML data pipelines. We focus on DataOps’ integration of metadata, data version control, orchestration, observability, and quality checks to support reproducible and traceable data flows. Tools and patterns such as Apache Airflow for pipeline orchestration and the Stage–Transform–Consume pattern are discussed for orchestrating modular and stable data processing aycdata.comMedium. We also examine how statistical process control and monitoring reduce pipeline failures, and how version control frameworks borrowed from software engineering ensure auditability and reproducibility. The methodological framework blends literature review, case analysis, and synthesis of architectural patterns. This analysis underscores how DataOps transforms brittle ML pipelines into orchestrated, visible, and maintainable systems, and identifies current limitations and areas for further maturation before 2020.