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The Modern Data Engineer's AI Handbook: Proven Techniques to Design, Automate, and Optimize Data Systems for Machine Learning.
Paperback
Publisher: Independently Published
Published: Oct 24 2025
Pages: 574
Weight: 2.17
Height: 1.16 Width: 7.00 Depth: 10.00
Language: English
What makes some AI systems accurate, scalable, and trustworthy, while others collapse under real-world complexity? The difference isn't in the models. It's in the data engineering.
Through practical architectures, working code examples, and field-tested labs, this book shows how to connect the dots between raw data, intelligent features, and model outcomes. You'll learn to design hybrid pipelines, manage feature stores, enforce data contracts, detect drift, and optimize for both cost and latency, without sacrificing governance or agility.
You'll learn how to:
- Build and automate end-to-end data pipelines for both batch and streaming workloads.
- Design modern lakehouse and feature store architectures that support real-time and offline ML use cases.
- Implement data contracts, versioning, and testing practices that eliminate pipeline fragility.
- Deploy scalable MLOps workflows with CI/CD, retraining triggers, and drift monitoring.
- Optimize infrastructure for performance, cost, and reliability across hybrid or multi-cloud environments.
- Apply real operational playbooks, covering lineage, security, auditing, and performance tuning that scale in enterprise AI systems.
If you've ever asked:
- How can I prevent data drift from silently breaking my models?
- What's the right way to design feature pipelines for both training and inference?
- How do I balance performance, scalability, and cost in real-world AI pipelines?
Equip yourself with the skills and systems every modern AI team needs.
Start building reliable, adaptive, and intelligent data pipelines today with The Modern Data Engineer's AI Handbook.
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