An increasing number of open-source and commercial vendors are attempting to automate machine learning, and analytics leaders need to know how this impacts data science and machine learning in their organizations. In this course, machine learning specialist, trainer, and author Keith McCormick dives into what the technology can and can't do and raises important questions about team structure and organization. Keith introduces AutoML and the machine learning (ML) lifecycle. He explains why some parts of that lifecycle—such as defining the problem—cannot be automated. Keith covers stages in the ML lifecycle, with a focus on which stages have been automated successfully and which require human support. He compares model accuracy and business evaluation, then shows you how AutoML can save you time and effort in model monitoring and maintenance. Plus, Keith goes over the wide variety of AutoML options that are available to you and offers advice for team composition.