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Livre 12 sur 20: Production AI Engineering Series
Synopsis
The Definitive Engineering Guide to Production Reasoning AI
Reasoning models are rewriting what AI can do. DeepSeek-R1, OpenAI's o3 series, and Anthropic's Claude—the frontier systems of 2025 and 2026—don't just predict the next token. They think. They self-correct, backtrack, and decompose complex challenges into logical steps to outperform systems trained purely on prediction.
Reasoning Model Engineering is the practitioner's guide to this new paradigm. It covers the theory of inference-time compute scaling, the practical engineering of chain-of-thought systems, and the hard-won lessons of production deployment—latency management, cost control, evaluation, and safety.
What You Will Master:- Theoretical Foundations: RLHF, process reward models, and Monte Carlo Tree Search.
- Prompt & Agentic System Design: Engineering prompts, tools, and contexts that unlock peak reasoning performance.
- Production Architectures: Deploying high-concurrency, latency-sensitive systems.
- Cost & Latency Optimization: Strategies for long-context, long-thinking-chain models.
- Evaluation & Safety: Frameworks that measure reasoning quality rather than simple output accuracy.
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Reasoning Model Engineering: Inference-Time Scaling and Chain-of-Thought Optimization for Enterprise AI Engineers (Production AI Engineering Series)
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Reasoning Model Engineering
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Reasoning Model Engineering
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Reasoning Model Engineering (Paperback)
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Paperback. Etat : new. Paperback. The Definitive Engineering Guide to Production Reasoning AIReasoning models are rewriting what AI can do. DeepSeek-R1, OpenAI's o3 series, and Anthropic's Claude-the frontier systems of 2025 and 2026-don't just predict the next token. They think. They self-correct, backtrack, and decompose complex challenges into logical steps to outperform systems trained purely on prediction.Reasoning Model Engineering is the practitioner's guide to this new paradigm. It covers the theory of inference-time compute scaling, the practical engineering of chain-of-thought systems, and the hard-won lessons of production deployment-latency management, cost control, evaluation, and safety.What You Will Master: Theoretical Foundations: RLHF, process reward models, and Monte Carlo Tree Search.Prompt & Agentic System Design: Engineering prompts, tools, and contexts that unlock peak reasoning performance.Production Architectures: Deploying high-concurrency, latency-sensitive systems.Cost & Latency Optimization: Strategies for long-context, long-thinking-chain models.Evaluation & Safety: Frameworks that measure reasoning quality rather than simple output accuracy. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798183820102
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Reasoning Model Engineering : Inference-Time Scaling and Chain-of-Thought Optimization for Enterprise AI Engineers
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Taschenbuch. Etat : Neu. Neuware - The Definitive Engineering Guide to Production Reasoning AIReasoning models are rewriting what AI can do. DeepSeek-R1, OpenAI's o3 series, and Anthropic's Claude-the frontier systems of 2025 and 2026-don't just predict the next token. They think. They self-correct, backtrack, and decompose complex challenges into logical steps to outperform systems trained purely on prediction.Reasoning Model Engineering is the practitioner's guide to this new paradigm. It covers the theory of inference-time compute scaling, the practical engineering of chain-of-thought systems, and the hard-won lessons of production deployment-latency management, cost control, evaluation, and safety.What You Will Master: - Theoretical Foundations: RLHF, process reward models, and Monte Carlo Tree Search.- Prompt & Agentic System Design: Engineering prompts, tools, and contexts that unlock peak reasoning performance.- Production Architectures: Deploying high-concurrency, latency-sensitive systems.- Cost & Latency Optimization: Strategies for long-context, long-thinking-chain models.- Evaluation & Safety: Frameworks that measure reasoning quality rather than simple output accuracy. N° de réf. du vendeur 9798183820102
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