Free · Open · Community-maintained · CC BY-SA 4.0
ScaledAIOps is a community-maintained reference for engineering, data and product teams: six disciplines, eight principles and eight roles that describe how organisations operate AI systems reliably at scale. No vendor, no certification.
Traditional Software Development is code-centric and deterministic. ScaledAIOps operationalizes probabilistic AI systems through continuous feedback loops, governed data pipelines, and full model lifecycle observability.
The "Continuous Feedback Loop" you see below isn't just a lifecycle concept — this site runs it: see how scaledaiops.org resolves its own feedback with FFRS.
Want to use this animated diagram in your slides or documentation?
Three layers. Click any tile to go deeper.
What teams must do
Build, train and serve models reliably.
Govern models from development to retirement.
Data quality, lineage, access and governance.
Monitoring, SLOs and incident response for AI.
Responsible AI, threat modelling, regulation.
Business value, teams and adoption.
How they should think
Every page is a Git-tracked, CC BY-SA document. Fix a sentence or propose a practice.
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