An architecture review for an AI system catches design flaws at the cheapest possible stage: before implementation. A data pipeline that cannot handle the expected volume, a model serving architecture

A video streaming platform grew from 1,000 beta users to 10 million subscribers over thirty months. Their recommendation system was rebuilt three times during this period. Each rebuild was triggered n

Every AI team I have worked with has a graveyard of projects that should have been killed early but were not. A chatbot that no one uses. A recommendation engine that does not outperform a simple heur

Data pipelines break because data producers and data consumers have different assumptions. The producer assumes the consumer can handle null values in a column. The consumer assumes the column is neve

The market for AI engineers is the tightest it has been since the deep learning boom of 2017. Demand has grown 280% year-over-year for the "AI engineer" title, and the supply of experienced practition

Your iPhone prompts you: iOS 18.4 is available. It includes improvements to battery performance, new photo editing tools, and a fix for crashes in third-party apps. You can install it now or wait. If

Basic retrieval-augmented generation works well in demos and poorly in production. The demo shows a clean pipeline: chunk text, embed chunks, retrieve relevant chunks, feed them to the model. The prod

Publishing aggregate statistics about a dataset sounds safe. The average salary in a department. The number of users in a geographic region. The distribution of query types in a search engine. But agg

Five years ago, "data company" described a specific type of organization: a business whose primary product was data or data services — Snowflake, Databricks, Palantir, Bloomberg. Today, the distinctio