A non-commercial, community-driven initiative to define how organizations operationalize AI at scale.
ScaledAIOps is an open framework that provides a structured, practitioner-tested approach to building, deploying, and operating AI systems across the enterprise. Inspired by frameworks like the Scaled Agile Framework (SAFe), it brings the same level of rigor, shared vocabulary, and operational discipline to the emerging challenges of AI Operations.
Traditional DevOps assumes deterministic code: if tests pass and the binary compiles, behavior remains static. In contrast, AI systems depend on live data distributions, probabilistic inference, and continuous drift. ScaledAIOps bridges this fundamental shift by establishing operational feedback loops across data, platform, and governance.
Organizations adopting AI face a common set of challenges: fragmented tooling, unclear ownership between data science and engineering, inconsistent deployment practices, and difficulty measuring real-world impact. ScaledAIOps addresses these gaps by providing a comprehensive reference that teams can adapt to their context.
ScaledAIOps is hosted on GitHub under the Scaled-AIOps organization. You can contribute by:
Feedback handling on this site is a live case study for the Fast Feedback Resolution System (FFRS) — a five-stage pipeline with an AI agent producing the first response. Read how it works, or follow the write-up in progress: the FFRS paper (work in progress, numbers pending).
All content is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).