Engineering AI at Scale: Building Fast, Deterministic Production Pipelines
Build High-Performance, Production-Grade AI Pipelines That Scaling Systems Can Actually Depend On.
Moving AI models from research to production is where most modern software systems break down. While training a model or making an API call is easy, building reliable infrastructure that processes high-throughput data with predictable performance, minimal latency, and zero non-deterministic failure is a massive engineering challenge.
In Engineering AI at Scale, Ethan Crossley delivers a practical, blueprint-driven guide to architecting deterministic, resilient AI infrastructure behind modern applications.
Whether you are scaling LLM workflows, handling real-time inference pipelines, or managing distributed data orchestration, this book bridges the gap between machine learning concepts and rigorous software engineering practices.
Inside, you will discover:Deterministic Pipeline Design: How to eliminate unpredictable race conditions, unhandled edge cases, and non-deterministic behavior in complex AI systems.
Latency & Throughput Optimization: Strategies for building low-latency, high-availability pipelines that handle massive concurrent requests without crashing under load.
Scalable Data Orchestration: Practical patterns for chunking, streaming, caching, and processing large datasets efficiently across distributed environments.
Observability & Error Recovery: How to set up effective logging, trace execution flows, and build automated fallbacks for brittle third-party APIs and model failures.
Production-Ready Architecture: Real-world architectural blueprints for integrating vector stores, queuing systems, and asynchronous workers into existing backend systems.
Stop relying on brittle scripts and fragile API wrappers. Learn how to design, build, and deploy AI infrastructure that scales seamlessly with your business.
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