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Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
Paperback
Publisher: Independently Published
Published: Oct 28 2025
Pages: 344
Weight: 1.32
Height: 0.72 Width: 7.00 Depth: 10.00
Language: English
Every data professional has faced it: a model that performs perfectly in testing but unravels in production. The culprit isn't magic; it's weak data foundations. Without structured, governed, and observable data pipelines, even the smartest algorithms crumble under drift, latency, and inconsistency.
Through real-world patterns, reproducible frameworks, and field-tested strategies, this book shows how to architect systems where data quality, versioning, observability, and scalability are built in, not bolted on. It bridges the gap between data engineering, data science, and MLOps, helping you create infrastructure that empowers, not obstructs, your models.
You'll learn how to:
- Design scalable data pipelines that serve both training and inference workloads.
- Build feature stores that ensure consistent, reusable model inputs.
- Enforce data contracts, lineage, and quality gates across every stage of the pipeline.
- Implement versioning, reproducibility, and rollback strategies that make audits effortless.
- Monitor data and model drift in production before performance collapses.
- Align data engineering and machine learning teams through shared metrics and SLAs.
If you're a data engineer, machine learning practitioner, or technical leader tired of firefighting broken pipelines and inconsistent results, this book delivers the frameworks and practices you need to build dependable, production-grade AI systems.
Build your competitive edge on reliable data, not reactive fixes.
Your AI models are only as strong as the pipelines beneath them, make them unbreakable.
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