Writing
Essays on data infrastructure, AI deployment, and the practice of consulting.
01
Why most enterprise AI fails in the boring middle
The problem is rarely the model. It is the twelve steps between the model and a decision that someone will act on.
Data Engineering · 2026 · 8 minute read8 minute read
02
The economics of a data pipeline rebuild
When a client asks how long a rebuild will take, the honest answer starts with measuring what the current system actually costs.
Infrastructure · 2025 · 11 minute read11 minute read
03
On anonymization: what you can and cannot hide
We have anonymized client data for five years. Here is what we have learned about what is genuinely safe and what only appears safe.
Practice Note · 2025 · 6 minute read6 minute read
04
The pipeline failure modes no one talks about
Silent failures are the dangerous ones. Here are the four failure patterns we encounter most often, and how we instrument for them.
Infrastructure · 2024 · 9 minute read9 minute read
05
What "production-ready" actually means for an ML model
It is not about accuracy. It is about the eleven properties a model must have before you can safely operate it.
AI Systems · 2024 · 13 minute read13 minute read