Automated data annotation is a way to use AI-assisted tools and software to accelerate and improve the quality of creating and applying labels to images and videos for computer vision models. This process is crucial as it impacts the accuracy, outputs, and results that algorithmic models generate. Automated data annotation can take manual, human-produced labels and apply them across vast datasets, making a project run much smoother and faster. The guide covers different types of automated data labeling, use cases, best practices, and how to implement automated data annotation more effectively with tools such as Encord. It highlights the importance of cost-effectiveness, speed, consistency, and objectivity in the annotation process, and provides features to look for in an automated data annotation tool, including support for model or AI-assisted labeling, different types of data and file formats, easy-to-use tools with collaborative dashboards, data privacy and security, automated data pipelines, customizable quality control workflows, and training data and model debugging.