| Management number | 219166512 | Release Date | 2026/05/03 | List Price | $12.64 | Model Number | 219166512 | ||
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How many more times are you willing to watch an “AI demo” fall apart the moment it hits production?The prototype looks perfect on your laptop and the demo wins the room.Then reality hits: unpredictable latency, runaway costs, inconsistent outputs, sudden regressions, and incidents that burn weeks.If you’re building with LLMs, RAG, or agents, you already know the truth: it’s not “the model.”The real problem is the production system missing around the model.The AI Engineering Sistems is a step-by-step guide to building, deploying, and scaling production AI systems—plus an actively maintained GitHub repo you can clone and ship.Here’s a small fraction of what you’ll learn:How to think like an AI Engineer so you deliver operable systems instead of fragile prototypesHow to treat model APIs as volatile dependencies so downtime and regressions stop blindsiding youHow to implement routing and fallbacks so your product keeps working when a provider degrades or failsHow to build reliable Retrieval and RAG so hallucinations drop and trust goes upHow to design for scale so queues, workers, caching, and budgets stay under controlHow to make evaluation the operating system so you catch failures and regressions before users doHow to improve observability and safety so logs, traces, metrics, and defenses reduce real-world risk“I don’t have time—I need something practical.” It is: patterns, skeletons, contracts, and workflows you can apply immediately.“My stack is different—this won’t fit.” It will: the principles are stack-agnostic and the components are modular.“I’ve already read LLM books.” This isn’t theory—it’s release engineering for behavior, built for production.If you want to stop fighting fires and start running AI like a dependable system, this is the operating manual you’ve been missing.Buy now on Amazon and start shipping production AI you can actually deploy, operate, and scale. Read more
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