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Efficient AI Engineering: Tools, Patterns, and Best Practices by Dave Richardson
English | September 17, 2025 | ISBN: N/A | ASIN: B0FRNLYCYF | 121 pages | EPUB | 1.55 Mb
Artificial intelligence is no longer just an experiment-it's the backbone of modern products, platforms, and enterprises. Yet building AI that is accurate, scalable, and reliable is not enough. Today's engineers face a new challenge: efficiency. How do we design AI systems that deliver maximum impact with minimal cost, compute, and complexity?
Efficient AI Engineering: Tools, Patterns, and Best Practices is a comprehensive guide for practitioners who want to move beyond proofs of concept and build AI systems that truly work in production. Blending practical engineering wisdom with state-of-the-art techniques, this book shows you how to streamline every step of the AI lifecycle-from data preparation to model deployment and monitoring-without sacrificing performance.
Inside, you will discover:Foundations of efficiency in AI: principles, trade-offs, and lifecycle checkpointsData engineering practices for clean, reproducible, and cost-effective pipelinesModel optimization techniques including distillation, quantization, and lightweight architecturesDeployment patterns and MLOps workflows for scalable, reliable AI in productionMonitoring, security, and compliance strategies to ensure trust, safety, and resilienceCase studies and anti-patterns drawn from real-world successes and failuresFuture directions in efficient AI, from sustainable compute to human-AI co-engineeringThis book equips you with the tools and patterns to transform experimental models into efficient, production-ready AI systems.
Practical, forward-looking, and deeply grounded in engineering best practices, Efficient AI Engineering is your roadmap to building AI that scales responsibly and sustainably in the decade ahead.
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