Data Engineering Fundamentals for ML Pipelines

Most machine learning failures in production are really data failures, and few courses teach you how to prevent them. This field guide gives you the data engineering habits that make ML pipelines fresh, correct, and trustworthy.Data engineering is the part of machine learning that most courses skip. You learn to train a model, evaluate it, maybe deploy it from a notebook. Then you get to work and someone hands you a feature table that stopped updating three days ago, a training set with a subtle leak, or a validation step nobody wrote. The model is fine. The data is not.Data Engineering Fundamentals for ML Pipelines is a practical path from spreadsheets to production ML data. It covers the engineering that surrounds a model and decides whether the data arriving at it is fresh, correct, and consistent with what it was trained on. The discipline has a shape: define the data you need before writing code, land raw data before transforming it, gate every transformation with validation, make writes idempotent, monitor what matters, and write the runbook before going on call.Eleven chapters build that discipline step by step: from SQL queries to pipelines, the six organs of an ML data pipeline, data sources, the SQL and Python tools you will actually use, schemas and data contracts, data quality and validation, orchestration, features and feature stores, storage choices, and monitoring with an on-call mindset. The book closes with a worked example of your first production pipeline.Try This exercises, Quick Checks, Key Takeaways, starter schemas, and a SQL pattern catalog make it both a course and a reference. Book 3 of the ML Engineer's Journey, it is written for career switchers, early-career data engineers, and ML practitioners who have realized their production problems are really data problems.