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In the second article in our AI risk management series, we pivot our focus to a vital element in the context of ML systems: organizational processes.

This article introduces a three-part series on AI risk management. It discusses cultural competencies that can prevent and mitigate AI incidents, with a focus on promoting responsible AI practices.

As Vinita Bansal’s job shifted from coding to managing people, and then managing managers, she gleaned five crucial lessons through experimentation, trial, and error.

As your company maps out its 2024 AI strategy, use this powerful metaphor to consider the implications of centralized knowledge and striking a balance between knowledge graphs and LLMs.

Terminology in the data field is a mess. This article clears up the confusion – and responsibilities and skills required – using a series of Euler diagrams.

This article explores key factors for comparing machine learning solutions, providing an improved approach to model comparison beyond predictive power.

This article explores a short-list of techniques as you seek to adopt generative AI at the enterprise level. Use it as a checklist for patterns and resource for talent strategy

Drawing from a decade of experience, Armin Kakas details five lessons and a roadmap for analytics leaders who want to build a sustainable practice that adds real, quantifiable value.