IntDEx framework enhances AI SDLC with continuous monitoring for model drift

The IntDEx framework is proposed as a method for continuous monitoring of AI delivery engines, addressing the challenge of model behavior changing post-release without code modifications. Unlike traditional SRE practices focused on operational metrics, IntDEx integrates monitoring into the AI SDLC, tracking not only correctness but also concept drift, hallucinations, data quality, AI safety, cost, sovereignty, and ethics. It establishes a regression baseline using accepted deliverables and makes threshold breaches mandatory blocking checkpoints for human review, aligning with frameworks like NIST AI RMF and the EU AI Act. AI

IMPACT Establishes a new paradigm for AI system monitoring, integrating it into the delivery lifecycle to manage post-release behavioral drift.

RANK_REASON The item describes a proposed framework and its methodology for continuous monitoring of AI delivery engines, drawing on existing research and frameworks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• AI SDLC
• Breck et al.
• Chen
• Ewaschuk
• Gama et al.
• Georgia Kali
• GPT-4
• IntDEx framework
• SRE book
• Zaharia
• Zou

• product
• infra

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