About this book:

This book provides a comprehensive guide to building reliable, scalable data infrastructure for machine learning, emphasizing that data quality and engineering are the true bottlenecks in AI success. It covers foundational topics such as data modeling for ML—focusing on entities, events, and point-in-time correctness—and explores storage systems and columnar file formats like Parquet and ORC, enhanced by open table formats (Delta Lake, Iceberg, Hudi) that enable ACID transactions, schema evolution, and time travel. The text details architectural patterns including the medallion architecture (Bronze, Silver, Gold layers), data lakehouse, streaming and batch processing trade-offs, and the critical role of feature stores in eliminating training‑serving skew by providing consistent offline and online feature serving.

Practical pipeline design is examined through chapters on reliable ingestion, ETL/ELT patterns, orchestration with DAGs (using tools like Airflow, Prefect, Dagster), and robust data validation and quality gates implemented as layered checks across the medallion layers. The book stresses observability—metrics, logs, traces, SLAs/SLOs—and proactive drift detection (data, feature, concept) to maintain model performance in production. Additional topics include dataset versioning and reproducibility, labeling and weak supervision, privacy/security governance, testing and CI/CD for data pipelines, cost and performance optimization, multi‑cloud and hybrid architectures, and integration with MLOps for end‑to‑end model lifecycle management. Real‑world case studies illustrate production‑grade systems for personalization, fraud detection, predictive maintenance, and content moderation, while anti‑pattern chapters help engineers avoid common pitfalls like feature jungles, monolithic pipelines, and unversioned data. The final chapter offers a roadmap for evolving data platforms and teams, advocating continuous learning, modularity, and a data‑as‑a‑product mindset to sustain impactful, responsible AI at scale.

What You'll Find Inside:

• Design scalable ML data platforms using lakehouse, medallion, and streaming architectures.
• Build reliable ingestion pipelines with idempotency, validation, and fault tolerance.
• Engineer features and operate feature stores to ensure consistency between training and serving.
• Achieve reproducibility via dataset versioning, time travel, and data lineage tracking.
• Integrate data engineering with MLOps for automated testing, CI/CD, and model lifecycle management.

Who's It For:

This book is for data engineers, ML engineers, and data scientists who need to build, operate, and scale data infrastructure for machine learning. It is ideal for professionals responsible for designing pipelines, feature stores, and datasets that support training and inference, ensuring data quality, reproducibility, and low-latency serving. Readers will gain practical patterns and tools applicable to batch, streaming, and hybrid environments.