EDITOR’S NOTE: This article is adapted from the new preface to the paperback edition of The AI Playbook: Mastering the Rare Art of Machine Learning Deployment., coming October 27, 2026. Special offer: Pre-order the paperback now and receive free, immediate access to the audiobook. This article was originally published in CDO Magazine. An epic
EDITOR’S NOTE: This article is excerpted from The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The paperback edition of this book will drop on October 27, 2026, along with a new preface. Special...
EDITOR’S NOTE: This is the preface to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The paperback edition of this book will drop on October 27, 2026, along with a new, second preface,...
Originally published in Forbes The great potential of LLMs is significantly compromised by their Achilles heel: a deadly reliability problem. Predictive AI can address this problem – and that represents the next killer app for predictive AI. Enterprises...
Originally published in Forbes On a recent episode of the Dr. Data Show, my co-host Luba Glouhova and I tackled a new paper authored by AI luminary Yann LeCun alongside other researchers. We had been tipped off by another...
A practical introduction to making complex project structure explicit for humans and AI, with examples from predictive analytics and enterprise ML. Large analytics and AI projects contain more than source code. Predictive analytics and enterprise ML projects...
Originally published in Forbes Recently on The Dr. Data Show, my co-host Luba Gluhova and I dug into the evolving discourse surrounding artificial general intelligence – and its stubborn incoherence. A recent publication by the venture capital firm Sequoia...
Originally published in Forbes The AI executives are at it again, promising human-level machines in the near future. In Davos, the CEOs of Google DeepMind and Anthropic each doubled down on the near-term arrival of artificial general...
Originally published in Forbes When Henry Castellanos first presented his machine learning model to his company’s executives, he found himself fighting off a certain self-doubt that is so common among data professionals, it’s almost universal. On one...
Originally published in Forbes Most predictive AI projects fail to launch into production. The number crunching is sound and the data scientist delivers a viable machine learning model – but stakeholder objections sadly preclude deployment. To better meet stakeholders where they are,...
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