Without training data, there is no machine learning model.
Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, and many other AI and robotics applications.
Data annotation is the procedure of processing unprocessed raw data, including voice, picture, text, video, etc., and converting it into a structured one that can be recognized by an AI algorithm. Data annotation is a process in which a data annotator uses annotation tools to structure data so as to empower the AI model.
1. Workflow system
In a narrow sense, data annotation refers to the operation of pulling frames, tracing points, and transferring the raw data, but in a complete annotation loop, the annotation process is only a part.
Under normal circumstances, a complete annotation project, from the beginning to the end, requires multiple processes such as build custom annotation tool, scripts for pre-processing data, project creation, labeler training, worker performance tracking, data security and compliance, quality inspection, data delivery, and so on. Each individual process can be subdivided into more detailed workflows.
Taking project creation as an example, the following steps need to be completed from new creation:
New Project — Upload Data — Requirements Management — Annotations Scheme — Processing — Annotations Result in Export Settings — Release Project.
For project managers, a perfect and smooth workflow system is of great significance to project management.
The whole-process workflow system can effectively help the project team control the project, avoid unnecessary costs, and increase operational efficiency.
2. Different Role Players
From the perspective of role configuration, data annotation platform participants can be roughly divided into annotator, auditor, quality inspector, administrator (project manager, representative of Party A), etc.
Different roles have different authorities, corresponding to different work and levels. Take the annotator as an example, the work is to follow the guideline and accomplish basic annotation tasks. The annotator cares more about the amount of data completed, rejected, and qualified, as these are related to their own income.
Project managers, on the other hand, are more concerned about big pictures such as project completion, data quality, role authorization assignment, project schedule, and so on.
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Machine learning success depends on the human workforce, however, a person’s energy is always limited. The more data he or she is exposed to, the greater the probability of missing data and error will be. Therefore, platform data visualization becomes particularly important.
Automation management connects different roles, generates customized data service, and helps different roles quickly grasp the project operation, not only shorten the time required to understand the project but also can decrease the error problems.
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
Automation management: task splitting algorithm
ByteBridge divides the complex work automatically into simple small components to further reduce human error.
On the dashboard, clients can set labeling rules, iterate data features, attributes and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.
For further information, please visit our website site:ByteBridge.io