Engineering notes
Practical engineering notes on embedded validation, agentic systems, and machine learning.
Twenty in-depth articles on building reliable automation, reviewable AI workflows, and maintainable ML systems.
Featured · Jul 22, 2026
Before Training a Custom ML Model, Prove the Problem Is Learnable
A feasibility study should test the decision, labels, data, baseline, error costs, and deployment constraints before a team invests in a custom model.
Jul 21, 2026
Edge AI Is a System Trade-Off, Not a Model Export
A model that runs on a workstation is not automatically an edge solution. Hardware, latency, memory, power, thermals, preprocessing, updates, and recovery must be designed together.
Jul 20, 2026
Computer Vision Fails in Conditions, Not in Averages
An overall score can hide the exact lighting, motion, device, or product condition that breaks a vision system. Evaluate the operating envelope as a matrix of real conditions.
Jul 11, 2026
Data Drift, Concept Drift, and Sensor Drift Are Different Problems
A changed input distribution, a changed relationship between input and outcome, and a degraded measurement system may look similar in a dashboard but require different evidence and remedies.
Jul 10, 2026
Monitoring an ML System When Ground Truth Arrives Late—or Never
When outcome labels are delayed, monitor the input contract, data pipeline, prediction behaviour, uncertainty, system health, review signals, and business process without pretending those proxies equal accuracy.