Data Engineering & Production Systems

A model is only as good as the pipeline feeding it. Most of the reliability, and most of the failures, live in the data layer: how features are computed, how the system scales, and whether the next engineer can change it without breaking it. These pieces cover that ground.

Know the stack before you reach for a tool

The Data Engineering Stack: A Practitioner's Map is the orientation I wish I had early: what each layer does and where the real complexity hides. Two skills underpin all of it and are worth having cold. SQL for Data Scientists covers the query patterns that actually come up, and Python Performance is about writing code that holds up when the data stops fitting in memory.

Feature pipelines are where leakage hides

Where and how you compute features decides both correctness and latency. BigQuery vs TensorFlow Transform walks through that choice and the trap that creates training and serving skew, the silent killer of deployed models. Getting this layer right is the difference between a model that matches its backtest and one that quietly drifts.

Training and shipping at scale

When data and models outgrow one machine, the engineering changes shape. Distributed Training in TensorFlow compares the strategies and their tradeoffs, and Building Production Machine Learning Pipelines with TFX is about the orchestration, validation, and monitoring that turn a trained model into a system you can trust to run unattended.

What "production" actually means

The gap between a notebook and a deployed system is wider than most people expect. The Machine Learning Project Lifecycle is about what really happens between "the model works" and "the model is in production and someone depends on it". That gap is where I do a lot of my work.

This is the foundation under Production Machine Learning & Data Infrastructure, applied at scale in the supply-chain forecasting and banking data automation case studies.