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Data Annotation: tooling & workflows latest trends
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The development of AI applications, from robotic perception to self-driving cars, require millions of data points to be annotated, often by human eyes and hands. In this post, iMerit, a leader in providing high-quality data for Machine Learning and AI, delivers its predictions on processes and technology improvements that will make data annotation cheaper and more efficient.  For a deeper dive into these trends and to speak to an expert who can help kickstart your data project, follow this link.  Predictive annotation tools are tools that can automatically detect and label items based on similar manual annotation. In computer vision workflows, these tools can annotate subsequent frames after the first few are manually marked. Human innovation is still required for QA and edge cases, the new key differentiator when selecting a data annotation partner.  Source: iMerit Customized reporting. While working with large expert data annotation teams, reporting on a project progress will become granular at an individual’s level, and dynamic through the use of APIs and open source tools. This will allow for informed decision making through the project’s lifecycle.  Focus on quality control. When working with large data sets, teams focused exclusively on edge cases and quality control will be built, comprising experts with deep ...Read More on Datafloq
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