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A Thorough, Practical Handbook for Developing, Implementing, and Enhancing Real-World AI Systems
As AI continues to capture headlines, many organizations grapple with a different situation: stalled initiatives, fragile infrastructure, expensive deployments, and a lack of a clear framework for constructing scalable, dependable systems.
The AI Engineering Bible directly addresses this issue.
Targeted at engineers, technical leads, AI architects, and product managers, this book delivers a transparent, methodical approach to creating production-ready AI systems—anchored in contemporary best practices, scalable infrastructure, and practical application.
Covering all phases of the AI lifecycle—from problem identification and data gathering to deployment, optimization, and ongoing maintenance—this resource offers the structure and technical depth required for professionals to confidently navigate AI initiatives at scale.
With this comprehensive guide in your possession, you will:Begin by precisely defining the problem and strategizing your AI system—from aligning objectives with business results to organizing architecture, data strategy, ethics, compliance, and human-AI interaction from the outsetConstruct every layer of your system with reliability as a priority, including data pipelines, preprocessing workflows, training loops, orchestration tools, and model selection—ready for integration into real-world software settingsDeploy your AI models into production confidently, utilizing containerized services, scalable cloud infrastructure, secure API integrations, and version-controlled workflows to minimize downtime and riskExpand your system to accommodate growing scale, employing proven tactics for distributed inference, federated learning, pipeline throughput, and load balancing—ensuring that your architecture develops without bottlenecksOptimize performance across every dimension, from latency and throughput to memory efficiency and cost-effectiveness, using cutting-edge strategies in tuning, compression, quantization, and system profilingGuarantee long-term reliability and adaptability through model monitoring, drift detection, retraining strategies, user feedback loops, governance frameworks, and continuous improvement processes that maintain system stability and effectiveness over time
Unlike other texts that focus narrowly on theory or specific tools, The AI Engineering Bible takes a comprehensive full-stack engineering approach—helping you bridge the divide between machine learning research and robust, maintainable production systems.
Whether you are tasked with building internal AI platforms, implementing customer-facing features, or expanding intelligent systems in critical environments, this book is crafted to support your endeavors with clarity and rigor.
If your aim is to deliver AI systems that are not just functional but also sustainable, secure, and scalable—secure your copy of The AI Engineering Bible and utilize it as your trusted technical reference for constructing systems that excel in real-world conditions.
From the Publisher
ASIN : B0F672GWRZ
Publisher : Independently published
Publication date : April 23, 2025
Language : English
Print length : 408 pages
ISBN-13 : 979-8281166782
Item Weight : 0.634 ounces
Dimensions : 8.5 x 0.92 x 11 inches
Best Sellers Rank: #12,928 in Books (See Top 100 in Books) #1 in Computer Programming Logic #2 in Cloud Computing (Books) #4 in Software Testing
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