Simor
Data Infrastructure for Production AI
Practical writing on AI data engineering, feature stores, and the infrastructure choices that determine whether AI systems work in production.
A healthcare analytics company received notice on a Tuesday afternoon that their primary AI infrastructure vendor was filing for Chapter 7 bankruptcy. The platform hosted their patient risk stratifica
A critical open-source library used by thousands of companies, including several Fortune 500 firms, is maintained by one person in their spare time. This is not a hypothetical. It is a description of
The guardrail problem in AI is a tension between two failure modes. Too few guardrails and the system produces harmful, inaccurate, or brand-damaging outputs. Too many guardrails and the system refuse
The traditional BI dashboard — a grid of charts that a business user opens every morning to check KPIs — is losing its grip on how organizations consume data. The decline is not dramatic. No one decla
You hold up a mirror to see if there is something on your face. The mirror does not clean your face. It does not tell you how to live. It reflects what is there so you can judge whether what is there
Serving a language model in production is an infrastructure problem, not a model problem. The model weights are the same regardless of how you serve them. What differs is throughput (how many requests
LinkedIn's latest workforce report shows "AI engineer" as the fastest-growing job title for the third consecutive quarter. Job postings containing the title increased 280% year-over-year. The growth r
An insurance company with $400 million in premium volume adopted data mesh two years ago. The central data team had become a bottleneck. Every business unit — claims, underwriting, actuarial, and dist
A Fortune 500 company hired a team of twelve machine learning engineers and tasked them with building a predictive maintenance system for their manufacturing floor. The ML team spent four months evalu