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 data engineer at an e-commerce company stared at a mess of SQL scripts, Python notebooks, and configuration files. What started as a simple ETL job had mutated into a hydra of interdependent transfo
A narrow bridge holds 50 cars safely. When car 51 tries to enter, the light turns red. Cars queue on the approach road, then the streets leading to it, then the highways beyond. The bridge is protect
A fintech company's data platform ground to a halt when a schema change cascaded through dozens of pipelines. Their homegrown orchestration system—a maze of cron jobs and bash scripts—offered no visib
You're sending a $10,000 check. Regular mail might get lost. Send two copies, recipient might cash both. What you need: tracked, signed for, proof of delivery. Your check arrives exactly once. Not zer
An energy company's AI predicted electricity demand would peak at 6 PM, as typical. The first game of the World Cup had millions turning on TVs at 4 PM, creating an unprecedented spike their models co
A parade where everyone maintains exact position. The drummer at position 10 stays at position 10. The flag bearer at position 50 remains at position 50. Even if they take breaks, when they reassemble
A pharmaceutical company's language model could discuss individual molecules but failed to understand that Drug A inhibited the same enzyme Drug B required for activation—a critical interaction that m
Central Library started small: one room, one librarian, manageable. Now it holds millions of books. Patrons wait hours. The librarian hasn't slept in weeks. The solution: split the library. Fiction (
A social media analytics company watched their Kubernetes cluster fail to handle traffic spikes from trending topics. The cluster would scale from 50 to 500 pods in minutes, but not fast enough to pre