Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Managing Production Large Language Models: Playbook for Designing, Deploying, and Operating LLM at Scale and Machine Learning FinOps Blueprints

Managing Production Large Language Models: Playbook for Designing, Deploying, and Operating LLM at Scale and Machine Learning FinOps Blueprints

O'Neal, Jordan

Paperback

Series: Enterprise Machine Learning Operations

ManagementGeneral ComputersProgramming

ISBN13: 9798904980153
Publisher: Cybersoft Publishing LLC
Published: May 5 2026
Pages: 454
Weight: 1.33
Height: 0.92 Width: 6.00 Depth: 9.00
Language: English
Written for ML architects, ML and LLM engineers, and technical leads, with platform and SRE engineers as a strong secondary audience.
Large language models fail in production in ways no load test anticipates. A retrieval-augmented pipeline answers confidently with fabricated citations when the vector index drifts. An agent loop burns a month of API budget in forty minutes because one tool call returned an unexpected schema. A prompt that cleared every red-team review gets hijacked by a malicious document in the first week of real traffic. Latency spikes vanish with no correlated metric because the KV cache was sized for a context window half as long as users actually send. These are the predictable failure modes of LLM systems, and teams that ship reliable LLM features design against them with patterns that hold across models, vendors, and inference frameworks.

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