This course provides a comprehensive, hands-on foundation in the core engineering principles required to build, evaluate, deploy, and maintain robust machine learning workflows. Moving beyond theoretical algorithms, students will focus on the practical end-to-end lifecycle of ML systems—from data preparation and rigorous model evaluation to deployment mechanics and post-deployment monitoring. By the conclusion of this course, learners will transition from data science practitioners to machine learning engineers capable of building reproducible, production-ready pipelines.