Data Annotation: tooling & workflows latest trends
As AI continues to boom, improved technologies and processes for data labeling and annotation are on the rise. iMerit, a leader in providing high-quality data for Machine Learning and AI, shares the latest trends in annotation workflow and tooling.
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.
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 understanding of the data and its subject matter. They will be able to function without detailed guidelines and will be hyper-focused on spotting and fixing issues within these large-scale datasets.
Workforce of SMEs. As more industries embrace the use of AI, the need for subject-specific data annotation teams will rise, in domains like healthcare, finance and the public sector. This focused but deep approach of the expert data labeler adds value to the annotation process, right from the validation of guidelines to the time of data delivery. It is also iMerit’s approach to training a high-skilled, in-house workforce of more than 3,500 with deep domain expertise.
A partner ecosystem is on the rise, with specialized vendors each providing differentiated value in the ‘data assembly line’, from data labeling to the creation of metadata, and the set-up of flexible and productive workflows. The ability to quickly navigate this big ecosystem for specialized capabilities will be mission-critical for the deployment of AI. iMerit has built an unique approach to working with partners, in a tool-inclusive manner.
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