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.
Publishing aggregate statistics about a dataset sounds safe. The average salary in a department. The number of users in a geographic region. The distribution of query types in a search engine. But agg
Five years ago, "data company" described a specific type of organization: a business whose primary product was data or data services — Snowflake, Databricks, Palantir, Bloomberg. Today, the distinctio
An agriculture technology company built a crop yield prediction model that combined satellite imagery, soil sensor data, weather forecasts, and historical yield records. The model predicted per-field
A pharmaceutical company I consulted for was three months into a project to fine-tune a large language model on their internal research corpus. The project had a team of four engineers, a budget of $8
A model registry is the version control system for your trained models. Without one, teams track model versions by filename, store artifacts in ad-hoc cloud storage locations, and discover which model
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-