Data Annotation Service: a Potential and Problematic Industry Behind AI | by ByteBridge

At present, data labeling has been powering various industries such as autonomous driving, agriculture, healthcare, retail, etc.

For example, Baidu’s AI data annotation center just accomplished a labeling project for facial recognition with masks during the covid-19 period. Data labelers need to mark key points on human eyebrows, eyes, and cheekbones so that AI scanners can identify human faces and measure their temperature while wearing masks.

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High-quality training data at scale

“We are eager to find reliable and cost-effective data labeling teams. The accuracy and quality of the processed data determine the outcome of our machine learning training test and final performance,” says Mr. Wang, an engineer in an AI company.

In fact, the strength of an AI system depends on the algorithm model and the quality and quantity of training data. It is showed that many AI companies use similar algorithm models, therefore, the quality and quantity of training data play a key role. In fact, getting high-quality labeled data is the toughest part of building a machine learning model. If the data quality is unqualified, the algorithm model cannot be well developed, AI company needs to label the data again. Timing is important, once the company is behind the schedule, the product may be overtaken by competitors.

Flexibility

In machine learning, in each round of testing, engineers would discover new possibilities to perfect the model performance, therefore, the workflow changes constantly. There are uncertainty and variability in data labeling. The clients need workers who can respond quickly and make changes in workflow, based on the model testing and validation phase.

Bytebridge, a blockchain-driven data company, has also realized such urgent problems in the data labeling industry and committed itself to empower AI development via its automated data labeling dashboard.

Accuracy

Dealing with complex tasks, the task is automatically segregated into tiny components to maximize the quality level as well as maintain consistency.

The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure efficiency.

Consensus mechanism: we assign the same task to dozens of workers for quality check, and the correct answer comes from the majority output.

All work results are completely screened and inspected by the machine and human workforce.

Flexibility

Developers can create their data collection and labeling projects on Bytebridge. The automated platform enables developers to customize various labeling projects and set labeling rules directly on the dashboard.

Moreover, developers can iterate data features, attributes, and task flow, scale up or down, make changes based on what they are learning about the ongoing project and how the AI model is performing in each step.

In addition, developers can check the processed data, speed, estimated price, and time on the visualized dashboard.