Open Daily: 10am - 10pm | Alley door 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

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Enterprise AI Observability and Monitoring: Monitoring, Governing Production AI Systems Drift Detection, LLM Monitoring, Agentic AI, Governance, and F

Enterprise AI Observability and Monitoring: Monitoring, Governing Production AI Systems Drift Detection, LLM Monitoring, Agentic AI, Governance, and F

Louis-Charles, Jordan

Paperback

Series: Enterprise Machine Learning Operations

ApplicationsDatabasesComputer Security

ISBN13: 9798904980078
Publisher: Cybersoft Publishing LLC
Published: Apr 30 2026
Pages: 354
Weight: 1.04
Height: 0.74 Width: 6.00 Depth: 9.00
Language: English
Your production AI systems are failing right now, and your monitoring stack cannot see it.
Every dashboard is green. Latency is within SLO. The inference endpoint returns a 200. But the fraud model trained on pre-pandemic data is scoring against a distribution that no longer exists. The recommendation engine drifted three sprints ago and nobody noticed. The LLM-powered support assistant started hallucinating policy details after a prompt template was promoted without regression testing. These are not hypothetical scenarios. They are live production incidents happening across every industry, and traditional DevOps observability was never designed to catch them. The gap between what your infrastructure metrics report and what your models are actually doing is where silent failures live, where revenue leaks, where compliance violations accumulate, and where trust erodes one undetected prediction at a time.

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