Modern Data Engineering for AI Applications: Build Reliable Pipelines, Feature Stores & RAG Systems That Power Production-Ready AI - Softcover

Inhaltsangabe

Most AI systems don’t fail because the model is weak. They fail because the data pipelines behind them were never built for production.

Modern Data Engineering for AI Applications is the practical field guide for engineers who need to move beyond demos and ship reliable, scalable data systems that actually power machine learning, feature stores, and retrieval-augmented generation. It shows you how to design and operate a single lakehouse foundation that serves both structured features and document embeddings—without the usual training-serving skew, silent drift, or costly full re-embeds.

Inside, you get a complete production blueprint: the five-stage pipeline that works at scale, proven tool combinations (Kafka, dbt, Spark, Feast, Iceberg/Delta, Great Expectations), incremental embedding patterns that cut cost 60–80%, hybrid retrieval with access-control that stays fast, and the observability, CI/CD, and disaster-recovery practices that keep systems recoverable in under four hours. Every chapter is grounded in working patterns, checklists, and thresholds used by teams processing millions of events—not theory.

What sets this book apart is its refusal to separate “data engineering” from “AI engineering.” You learn one coherent architecture where features, embeddings, quality gates, and online serving share the same source of truth, the same versioning discipline, and the same operational playbooks.

If you are ready to stop debugging pipelines at 3 a.m. and start shipping AI systems you can trust, this is the playbook. Open it, apply the patterns, and build the data foundation your models deserve.

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