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.
NVIDIA still dominates AI inference and training hardware, but the dominance is no longer absolute in the way it was 18 months ago. AMD has shipped competitive alternatives at lower price points. Inte
You are in a library built before computers. The building holds 200,000 volumes. You need a book on medieval water mills. You do not wander the stacks hoping to stumble on it. You walk to the card cat
Every enterprise processes documents. Invoices, contracts, forms, receipts, medical records, insurance claims — the volume is measured in millions of pages per month for large organizations. The quest
A healthcare conglomerate grew through acquisition for fifteen years. Each acquisition brought its own CRM. Salesforce in three divisions. Microsoft Dynamics in two. HubSpot in one. A custom-built CRM
At a mid-market insurance company with eight thousand employees, the Chief Data Officer and the Chief Technology Officer had fundamentally different views on how AI should be adopted. The CDO believed
Manufacturing figured out quality control decades ago. AI is still learning the lesson the hard way. When a car leaves the factory with a defect, the manufacturer does not shrug and say "models are p
LLM costs are easy to start and hard to control. A team ships a feature that calls GPT-4, the feature works, users like it, and the invoice climbs 15 percent month over month. The cost is not a proble
The benchmark results from the past quarter are hard to ignore. On tasks spanning legal document analysis, medical coding, financial risk assessment, and manufacturing quality inspection, vertical AI
Your daughter's math homework comes back with a red X. The answer is wrong. But she does not know why it is wrong, and the X does not tell her. She gets a correct answer on the next problem through lu