Tag: mlops
AI operations: the missing discipline
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.
LLM monitoring: Why your AI can be up while failing
Traditional monitoring tells you if your LLM is running. It does not tell you if it is delivering garbage to users. LangChain found 89% of organizations now implement observability, but evaluation adoption lags at 52%. Here is how to build LLM monitoring that catches quality failures in production.
Managing prompts in production
Your prompts are code. Treat them like it. LaunchDarkly found that teams lose hours figuring out which prompt version runs in production. Here is why version control, testing, and deployment pipelines matter more than writing perfect prompts.
Scaling AI to enterprise requires unlearning everything
Only 7 percent of organizations fully scale AI past the pilot stage, per MIT Sloan research. The approaches that work for 5 people become liabilities at enterprise scale for 50.
Why your AI pilots succeed but production fails
Pilots work because they are protected environments with dedicated resources. Production fails because it is the real world with real constraints. The gap is not technical - it is operational. Most AI pilots never reach production, not because the technology fails but because companies underestimate the operational readiness required.