Tag: monitoring

AI systems fail gradually and partially, not in clear binary states like traditional software. IBM research shows only 48% of AI models reach production. The model gives a plausible answer missing important context, latency spikes but stays under timeout limits, outputs degrade invisibly. Your AI error handling must match this reality.

Between technical MLOps and general business operations lies a missing discipline that determines whether AI creates lasting value or becomes expensive technical debt. With roughly 80 percent of AI projects failing in production, this ai operations framework applies Lean Six Sigma principles like continuous monitoring, quality assurance, and systematic improvement to AI systems at scale.