Gain a comprehensive introduction to machine learning engineering, exploring the essential processes, roles, and tools needed to build robust ML solutions. Learn how to move from concept to deployment-ready models with a focus on best practices and effective team collaboration.
This course covers the foundational aspects of machine learning engineering, including the taxonomy of data disciplines, the role of ML operations engineers, and high-level system design. Learners will explore the end-to-end ML development process, from initial concept through discovery, development, and deployment. The course also introduces the transition from individual models to scalable model factories, emphasizing feature engineering, automation, and monitoring for reliable production systems. Through clear explanations, real-world examples, and practical guidance, this course helps learners understand the core principles and workflows of machine learning engineering. Learners will build a strong foundation for designing, developing, and operationalizing ML solutions in professional environments. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Machine Learning Engineering with Python, by Andrew P. McMahon. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
