Writing
Essays on AI strategy, machine learning in practice, and what it actually takes to build organizations that benefit from both.
A practical walkthrough of building a multi-agent AI system that researches a topic, drafts course content, and evaluates its own output — with real lessons from building it.
The boardroom conversation about AI has outpaced most leaders' ability to evaluate what they're hearing. Here's how to ask better questions — and what the answers should sound like.
Tips and tricks for every level of practical data science — from the talk at ODSC East 2023 that resonated far beyond the conference room.
The pain of sharing code across notebooks is real. jupyckage solves it — one command, proper package structure, no boilerplate.
Most organizations treat AI ROI as a measurement problem. It's actually a strategic one. The questions you ask before you build determine everything that comes after.
A keynote from the GET Cities Kickoff Summit on what it takes to build technology that moves the world rather than just moving fast.
Data scientists run cross-validation constantly — but many do it without understanding why. Here's the full reasoning, from first principles to production.
Data science teams spend months building models that technically work — and fail to move the business. The culprit is almost always the same: an unstated assumption between output and outcome.
AI, machine learning, models, features — the words get thrown around in every boardroom. Here's what they actually mean, in plain language, with no condescension.