Writings
Essays on applied ML, uncertainty, simulation, and decision-making.
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What Your Model's Mistakes Are Trying to Tell You
The mistakes your model makes are one of the richest sources of signal in an ML project — and most teams never read them. -
What Is Correlation, Really?
A word that means something precise in statistics and something much vaguer in most business conversations — and why the gap matters. -
Why Your AI Project Failed (and It Wasn't the Model)
The hard part isn't getting your model to production. The hard part is keeping it useful once it's there. -
LLMs as Glue, Not Brains
Why the organizations getting real value from LLMs are using them as connective tissue — not as reasoning engines. -
From Models to Decisions: A Practical Mental Model for Applied ML
A conceptual framework separating prediction, uncertainty, dynamics, and decisions in real-world ML systems. -
Simulation: The Missing Layer Between Models and Decisions
Why feedback loops, delays, and tail risk often dominate outcomes — and why simulation belongs between models and real decisions. -
Why Uncertainty Matters More Than Accuracy
A practical essay on why point forecasts are often insufficient, and how uncertainty reshapes real business decisions. -
Why Accuracy Is Not Enough
Why many ML projects fail at impact: model metrics aren't business KPIs, and point forecasts aren't decisions. -
Statistical / Bayesian Inference vs. Machine Learning: Rivals or Teammates?
The opposition is overstated. The real question is what you trust more — your model structure or your data.