Production Data Engineering for Machine Learning: Designing, Building, and Operating the Data Foundations of Real-World ML Systems

Production Data Engineering for Machine Learning is the engineering manual for the architects, engineers, and team leads responsible for the data layer of production machine learning systems. The book treats data engineering for ML as a structural discipline distinct from both classical data warehousing and modeling—one organized around contracts, validation, lineage, feature infrastructure, and operational accountability for the data the model actually sees in production.

The book opens with the failure mode that motivates the field. At 2:47 on a Thursday morning, a fraud-detection model's precision score drops twelve points in four hours. The model code has not changed. The training pipeline ran. The feature store reports healthy. Three hours of investigation later, an on-call engineer traces the failure to a quiet upstream change: a new merchant-category code that the schema accepts but the model has never seen. From that opening, twelve chapters in six paired phases develop the patterns that prevent and detect those failures.

The Foundations phase (Chapters 1–2) covers data-quality dimensions that actually predict model behavior in production, schema design and evolution, and the contract discipline that turns upstream-system changes into negotiated events rather than silent regressions. The Modeling and Validation phase (Chapters 3–4) addresses the boundary between data engineering and ML: feature engineering as a platform discipline, feature stores, training-serving skew, online/offline consistency, validation suites that catch the failures that matter, and statistical drift monitoring. The Operations and Lineage phase (Chapters 5–6) covers ingestion architectures, batch and streaming patterns, lineage tracking, and the observability layer that makes data pipelines debuggable. The Serving and Security phase (Chapters 7–8) covers feature serving at scale, online latency budgets, privacy-preserving pipelines, differential privacy, and the security controls that keep regulated data inside the boundary it is supposed to stay inside. The Frontier Workloads phase (Chapters 9–10) extends the patterns to vector and embedding pipelines, retrieval-augmented generation data layers, and the emerging shapes of LLM and agentic data infrastructure. The Synthesis phase (Chapters 11–12) presents reference architectures and the operating model that scales the discipline from one team to an enterprise platform.

Every chapter opens with a concrete production scenario, develops the engineering response in vendor-neutral patterns, and closes with structured artifacts: a Technical Checklist of conditions to verify before production, an Owner Matrix that names the role accountable for each artifact, and a Team Discussion Guide that surfaces misalignment before it surfaces as an incident. Where specific tools appear, they make abstract patterns concrete; the patterns themselves outlive the tools.

Readers leave with the contracts, schemas, and validation patterns that prevent silent data regressions from reaching production, the feature, lineage, and observability infrastructure that makes pipelines debuggable, the privacy and security controls that keep regulated data compliant, and the operating model that scales the discipline across an enterprise ML portfolio.

© 2026 PublishDrive (E-bog): 9798904981044

Udgivelsesdato

E-bog: 31. maj 2026