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.
You have a 600-page book on regulatory compliance. You do not read it front to back. You scan the table of contents, identify the chapters relevant to your current question, read those chapters closel
A language model that only generates text is not enough for most enterprise problems. The real value emerges when an AI system can look up your customer record, check inventory levels across warehouse
A master woodworker takes on an apprentice. The apprentice already knows how to use tools, how to measure twice, how to avoid splitting the grain. What the apprentice needs is not general woodworking
You ask a research assistant: "What are the key clauses in our vendor contracts that affect data residency?" The assistant does not know off the top of their head. They go to the document store, find
A technology company built an impressive AI platform. They had GPU clusters, fine-tuning pipelines, evaluation frameworks, and a growing model registry. They opened access to any team that wanted to u
Mary Poppins reaches into her carpet bag and produces a lamp, a potted plant, a chair, and a full dinner service. The bag is impossibly large on the inside. But Mary does not reach past the top layer.
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