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DataScience Show Podcast
DataScience Show Podcast

End-of-Life for ML: A C-Level Playbook for Retiring, Replacing, and Decommissioning Models

06 June 2026 10:30 Mirko Peters

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About this episode

Enterprises often obsess over building models but under-invest in retiring them. This episode gives C-level leaders a clear playbook for knowing when to retire, replace, or decommission machine learning systems so they stop being liabilities and start being managed assets. I outline decision criteria tied to business impact, technical debt, compliance, and operational risk; governance patterns for controlled sunsetting; financial and organizational signals that tip the scale; and practical transition plans that minimize disruption to downstream teams and customers. Listeners will get concrete KPIs for retirement decisions, a step-by-step checklist for phased decommissioning, and leadership-ready talking points to align stakeholders across product, engineering, legal, and finance. The goal is to help executives convert accumulated model sprawl into actionable portfolio management that protects ROI, reduces exposure, and frees capacity for new innovation.

Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

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