Systems that were built, and what they taught back
Each post below takes one machine learning system that was actually built: what the problem really was, how the system was put together, the background needed to follow the field, how the industry solves the same problem, and what stayed useful once everything had shipped. The focus is on principles and architecture rather than step-by-step instructions.
Recommendation and semantic search for a content app
The four layers of a multimodal recommender: from raw content analysis to vector representations, hybrid retrieval, and multi-source ranking.
Face anti-spoofing: telling a real face from a replay
Four architectural approaches to presentation attack detection, a mechanism for separating style from content, and how the field standardized measurement.
A Vietnamese language model: predicting the next word
Three tokenization paths for Vietnamese, five architectures built from scratch, and the condition under which two perplexity numbers can be compared at all.
Vehicle detection and the road down to the edge
Detecting vehicles in bad weather on constrained hardware: two-tier augmentation, knowledge distillation, and the path down to the device.
Environmental monitoring: data infrastructure before models
Storing data that has both a time and a space dimension, forecasting each parameter on its own, and how the field measures forecast error properly.
Five principles that carry across every ML system
The engineering habits that hold their value in every domain: build checkable invariants, guard against leakage, set a frame of reference, choose the right measure, and keep the configuration with the artifact.