Engineering essays from the bench.
War stories, architecture decisions, and the occasional tirade. Written by the engineers who shipped it.
Bias and Fairness in Machine Learning: A Practical Guide
How to find, measure and reduce bias in ML models: where bias enters, choosing a fairness metric, Fairlearn code, LLM audits and monitoring in production.
How to Fine-Tune an LLM: When It Pays Off, LoRA and QLoRA
When fine-tuning an LLM beats prompting and RAG, how to build the dataset, train with LoRA or QLoRA, evaluate against a baseline and run adapters safely.
Supply Chain Predictive Analytics: A Practical Build Guide
How to build predictive analytics for a supply chain: censored demand data, forecast baselines, quantile models, reorder points and policy backtests.
What Is a Feature Store? A Practical Guide With Feast
What a feature store does, when you actually need one, and a worked Feast setup: point-in-time joins, online serving, materialization and skew checks.
Edge AI: Running ML Models on IoT Devices
Deploying ML on edge and IoT devices: model optimization, hardware selection, TensorFlow Lite, and real-world constraints.
