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Production AI Inference Engineering with Claude: Build High-Performance AI Applications with Claude and LLM Infrastructure
Paperback
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Publisher: Independently Published
Pages: 294
Weight: 1.13
Height: 0.62 Width: 7.00 Depth: 10.00
Language: English
Artificial intelligence is no longer defined by building powerful models alone. The real challenge is deploying them efficiently, serving millions of requests reliably, controlling infrastructure costs, and delivering fast, dependable experiences in production. That is where inference engineering becomes one of the most valuable skills in modern AI.
Production AI Inference Engineering with Claude is a comprehensive, hands-on guide that takes you beyond theory and into the engineering practices used to build scalable AI systems. Whether you're deploying Claude-powered applications, optimizing open-weight models, or designing hybrid AI infrastructure, this book provides the knowledge and practical techniques needed to build production-ready solutions with confidence.
Rather than focusing solely on APIs or isolated code examples, this book explores the complete inference lifecycle-from understanding how transformer inference works to optimizing latency, reducing GPU memory usage, implementing KV cache strategies, deploying high-throughput inference servers, and designing enterprise-grade AI architectures that remain reliable as demand grows.
You'll learn how Claude fits into modern AI infrastructure and how to integrate it with today's most powerful inference technologies. Along the way, you'll gain practical insight into how experienced AI engineers solve real production challenges, make architectural decisions, measure performance, and balance speed, scalability, security, and operational cost.
What You'll Learn- Build production-ready AI applications using Claude.
- Understand the complete LLM inference pipeline from tokenization to response generation.
- Optimize inference with FP16, BF16, INT8, GPTQ, AWQ, and other modern quantization techniques.
- Improve latency and throughput using KV cache optimization, prompt caching, continuous batching, and speculative decoding.
- Create reliable request pipelines with streaming, structured outputs, and tool use.
- Deploy high-performance inference servers using vLLM, TensorRT-LLM, and modern serving frameworks.
- Build hybrid AI systems that combine Claude with open-weight language models.
- Scale AI workloads across GPUs, containers, and cloud infrastructure.
- Monitor, secure, and optimize production AI services.
- Design AI platforms that remain adaptable as models, hardware, and infrastructure continue to evolve.
- Machine Learning Engineers optimizing inference performance and deployment.
- AI Engineers building Claude-powered applications and enterprise AI systems.
- Backend Developers expanding into modern AI infrastructure.
- MLOps and Platform Engineers responsible for deploying and scaling large language models.
- Software Engineers who want to understand what happens after model training.
- Solutions Architects designing scalable, secure, and cost-efficient AI platforms.
- Technical professionals looking to transition from AI experimentation to production deployment.
Whether you're developing AI assistants, coding tools, enterprise search platforms, customer support systems, document intelligence solutions, or intelligent automation workflows, the techniques in this book will help you build systems that are faster, more scalable, more reliable, and easier to maintain.
The future of AI belongs not only to those who build intelligent models, but to the engineers who know how to deploy, optimize, and scale them. Start building that expertise today.
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