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.
Every team building retrieval-augmented generation or semantic search eventually needs a vector database. The market has consolidated around four serious options: Pinecone, Weaviate, Qdrant, and Milvu
A manufacturing company with facilities in twelve countries ran its operational reporting on a traditional BI stack: a data warehouse, an ETL pipeline, and a dashboard tool that had been deployed six
In 2009, deploying software to production was an event. It involved a change request, a maintenance window, a runbook, and a prayer. Developers wrote code, then threw it over the wall to operations, w
Every AI infrastructure team eventually faces the same argument. One faction wants to build a custom solution because the commercial options do not handle their specific requirements. The other factio
Three releases in the last six weeks have redrawn the open-source LLM map. Meta shipped Llama 4 with a mixture-of-experts architecture that narrows the gap with proprietary frontier models. Mistral re
A security camera does not stop crimes. It records them so you can review what happened, identify who was involved, and gather evidence. After the fact, the footage becomes valuable for understanding
Traditional software monitoring tracks CPU utilization, memory consumption, request rates, and error counts. These metrics tell you whether your service is running and whether it is handling load. The
A legal technology company had invested six months building a retrieval-augmented generation system to help contract attorneys find relevant precedent clauses across a corpus of 180,000 executed agree
A logistics company processing two million shipments per day ran their entire operational reporting stack on nightly batch ETL. Every morning at 6 AM, operations managers reviewed dashboards built on