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 recommendation system team built their tenth model. Each model required feature engineering. Each feature engineering project started by copying code from the previous project, then modifying it for
You have a wall covered in photos. You are looking at one from a beach trip. Nearby are other beach photos, vacation snapshots, summer memories. Not identical shots, but related moments. The clusterin
A regional bank's investment research team spent 60% of their time gathering information and 40% doing analysis. Analysts had to search through regulatory filings, internal research memos, market data
You have a friend who is always certain. That friend will tell you, with complete confidence, that the Battle of Hastings was in 1067 (it was 1066), that water boils at 102 degrees Celsius at sea leve
The captain does not remember every moment of every voyage. The logbook does. What happened, when, what the crew observed, what decisions were made. When the captain reviews the log, past voyages info
A manufacturing company runs their operations on an ERP system installed in 2004. The vendor still supports it. The team knows how to maintain it. The integrations are stable. It works. The problem i
A speed camera does not stop the car. It captures an image at a specific moment, records the license plate and timestamp, and sends the data to a system where a human makes the judgment. The camera ob
A healthcare system deployed an AI triage assistant. It worked well in testing. In production, it started routing patients with chest pain to low-priority queues. The error was subtle and infrequent.
The buffet is unlimited in theory. You can make as many trips as you want. But the plate you carry is finite. Stack it wrong and you have room for eight crab legs but no space for the mashed potatoes