CRISP‑DM reframed for AI systems
A practitioner mapped the classic CRISP‑DM framework onto modern AI system lifecycles, from business understanding through deployment and orchestration, arguing the transition is smooth for data scientists moving to AI roles. The post included an infographic showing how each CRISP‑DM phase aligns with AI deliverables. (x.com)
A 1990s data-mining framework is being repackaged for 2026 artificial intelligence work, with one practitioner arguing that CRISP-DM still maps cleanly onto modern system delivery. (ibm.com) CRISP-DM, short for Cross-Industry Standard Process for Data Mining, breaks projects into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. IBM still describes it as a flexible life-cycle model whose phases loop back on one another rather than run in a straight line. (ibm.com) In a recent X post, practitioner Al Grigor laid those six phases over an artificial intelligence workflow and added newer delivery terms such as orchestration and production operations. The post’s central claim was that a data scientist moving into artificial intelligence engineering is not switching to a foreign process so much as renaming familiar deliverables. (x.com) That framing matches how machine learning operations groups now describe production work. The SIG MLOps community splits the work into design, experimentation and development, and operations, with business understanding and data understanding sitting at the front and deployment, testing, versioning, and monitoring at the back. (ml-ops.org) The practical change is not the disappearance of the old steps but the expansion of the last one. Modern machine learning operations guidance now treats deployment as a bundle of services that includes workflow orchestration, model versioning, continuous delivery, serving, and monitoring after release. (databricks.com) That matters for teams building large-language-model tools and retrieval systems, where “data preparation” often means curating internal documents rather than cleaning a spreadsheet. A 2025 project-management guide for generative artificial intelligence work makes the same point, arguing that chatbots and other assistants still need business goals, usable source data, evaluation, and deployment plans. (datascience-pm.com) The continuity is useful because CRISP-DM was built to be iterative, not linear. IBM’s documentation says teams routinely move back and forth between phases, which fits current practice where prompt design, retrieval tuning, guardrails, and model selection are revised after testing in production-like settings. (ibm.com) There is also a limit to the analogy. MLOps guidance puts heavier emphasis on reproducibility, automation, continuous deployment, and monitoring than the original late-1990s playbook did, because today’s systems depend on changing data, model registries, and live services that can drift after launch. (ml-ops.org; databricks.com) So the pitch in Grigor’s graphic is less that artificial intelligence erased the old process than that it stretched it. The familiar six-step skeleton is still visible; the modern additions mostly sit in the machinery wrapped around deployment. (x.com; ibm.com)
