August 2026
Intermediate to advanced
386 pages
10h 44m
English
Content preview from MLOps with Databricks
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Chapter 10. AI Governance
AI governance has become a central topic in recent years, as organizations try to balance fast progress in AI with the need for safety and responsibility.
On the one hand, companies have started taking a more cautious approach to customer-facing AI apps and begun investing in governance to avoid becoming the next negative headline. This shift was driven by several failures from early GenAI adoption without proper safeguards. A well-known example comes from New Zealand, where the supermarket chain Pak ’n’ Save launched an app designed to help customers use up leftovers. Users could input ingredients they had at home, and the app would generate a recipe. The project backfired when it suggested combining household chemicals into chlorine gas and described it as “the perfect non-alcoholic beverage to quench your thirst and refresh your senses.” This episode highlighted the reputational and safety risks of unchecked AI.
Meanwhile, governments, particularly in Europe, are taking the lead in setting AI regulations. The introduction of the EU Artificial Intelligence Act (EU AI Act) has pushed companies to carefully evaluate which applications they develop and deploy and make sure they comply with stricter requirements.
Beyond regulation and reputation, there’s also a hard business reality: today, AI is deeply embedded into critical applications across industries. A disruption in these systems can cause immediate and significant financial losses. This makes AI ...
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