Tag

model drift

5 articles

Latest in model drift

Blogs

When 200 OK Isn’t OK: Observability for LLM Systems

Traditional monitoring can show an LLM-powered application as healthy even when it delivers incorrect answers. Drawing on experience at Varo Bank, this article explains why AI observability must go beyond the four golden signals a…

Oct 23, 20263 min

All model drift Articles

Showing 1–5 of 5

Feb20

Why AI Makes Requirements a Runtime Artifact

In traditional software, requirements are static design-time artifacts. In AI-enabled systems, they must be continuously observed and enforced in production. Learn how AI collapses…

3 min

Feb20

What to do About AI’s Forced Rethink of Reliability in Modern DevOps

As systems become more distributed and AI-driven, traditional uptime metrics are no longer enough. The 2026 SRE Report shows how reliability is shifting toward user experience, spe…

3 min

Nov19

SRE in the Age of AI: What Reliability Looks Like When Systems Learn

As AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows…

3 min

Nov18

Blogs

The MLSecOps Era: Why DevOps Teams Must Care about Prompt Security

AI-driven software delivery introduces new risks, especially prompt manipulation within CI/CD workflows. This article details the emerging fields of PromptOps and MLSecOps and offe…

3 min