Automating the Machine Learning Lifecycle

О курсе

This course teaches you how to automate key stages of the machine learning lifecycle, from data transformation to model training and deployment. You will learn to implement conditional logic in SQL, build reusable and parameterized scripts, construct reproducible pipelines, debug data-quality issues, and automate model experimentation with robust version control. Throughout the course, you’ll benefit from a unique combination of perspectives—Packt’s clear, modular SQL instruction paired with Microsoft’s industry-level practices for data management and ML workflow automation. You’ll move from foundational SQL logic to full pipeline development, gaining the ability to connect datasets, code, parameters, and models in a traceable and repeatable manner. Whether you’re preparing data for machine learning, automating preprocessing steps, or managing model versions for deployment, this course gives you the practical tools and mindset to build reliable, scalable automation across ML workflows. If you’re already experienced, you will refine your ability to design maintainable, production-ready pipelines. If you’re still growing your skills, you’ll find structured, hands-on guidance that makes sophisticated ML automation techniques accessible and actionable.

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