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.
The regulatory landscape for AI safety has fractured along jurisdictional lines. The EU has taken a prescriptive, risk-based approach. The US has taken a sector-specific, agency-led approach. The UK h
Your office building has one electricity meter. At the end of the month, you get a bill for the whole building. You know the total cost of electricity for the month. You do not know which floor consum
When producers and consumers share a Kafka topic without agreeing on the data format, things break in production. A producer adds a field. A consumer expects the old schema. The deserialization fails,
The headline numbers are familiar. Women represent roughly a quarter of AI and data science professionals globally. At senior levels, the proportion drops to the low teens. At the C-suite level of AI-
A consumer goods company built an AI system that recommended reorder quantities for 12,000 SKUs across 340 distribution points. The system optimized for a multi-objective function that balanced invent
Code completion gets the attention, but it is the narrowest part of what AI can do in a development workflow. Walk into any team that has shipped software for a few years and they will tell you: writi
AI vendor procurement is where organizations make binding commitments that are expensive to unwind. A three-year contract with a model provider locks you into their pricing, their rate limits, their m
The combined AI infrastructure capital expenditure of the four largest cloud providers exceeded $100 billion in the trailing twelve months. Microsoft, Google, Amazon, and Meta are building data center
Two executives sit across a table. One speaks Japanese. One speaks German. The interpreter sits between them, translating in both directions. The executives do not need to know each other's languages.