Outsourcing the data annotation service and its current situation
There is no doubt that data annotation greatly benefits the machine learning algorithm to get trained accurately in the right direction.
However, data annotation is also repetitive, time-consuming and laborious considering the tremendous volumes of raw data required for machine learning algorithms.
Therefore, many ML companies choose to outsource it to data annotation service providers so that they can focus on the core business and technology development, which in turn helps to improve efficiency and competitiveness in the industry.
Considering a variety of outsourcing companies available in the data annotation market, here are 5 essential aspects when choosing an outsourcing company for data annotation:
1. Data quality: data quality is decisive for machine learning outcome
2. Costs: an affordable price that helps to save the budget.
3. Efficiency: finish the project in time or within a shorter period.
4. Flexibility: customize your unique data annotation service with fluent and fast communication.
5. Transparency: detect the ongoing process of the project
The traditional data annotation outsourcing workflow goes in this way:
a. defining the work and outlining the project’s requirement
b. looking for a trustworthy service provider and signing contracts on the project
c. the outsource company screening and training the skilled annotators
d. project underway
e. data quality verification and inspection
As you can see from the process, it usually wastes at least one more week on market research and project team building besides the actual data annotation working time, which lowers the efficiency unfortunately.
The cost of outsourcing covers extra expenses such as employee training, intermediate management, quality control. Of course, the cost also depends on the company you choose, the dataset size and annotation categories in order to calculate the team’s hourly or monthly fee.
Where to find the best data annotation outsource company?
If data annotation outsourcing service is meant to accelerate development of machine learning industry, why not provide the service in an automated, transparent platform where developers can manage and control the project in a dynamic way?
ByteBridge.io, a data training platform, makes it possible through blockchain technology and algorithms that eliminate the unnecessary costs and process.
In ByteBridge’s dashboard, developers can create the project by themselves, check the ongoing process simultaneously on a pay-per-task model with clear estimated time and price.
(From the ByteBridge Dashboard Example, 7,200 images out of total 10,800 images have been processed. Developers can download the result or pause the project anytime)
The datasets that ByteBridge.io collects, including but not limited to texts, images from reliable sources, videos and sound captured and recorded with specific scenes, are provided with the best quality and accuracy.
Compared to existing Western companies for data annotation outsourcing, ByteBridge.io's data labeling platform charges 90% lower whereas compared to companies in China and India, it offers 50% cheaper price. More than that, Bytebridge’s processing speed is more than 10 times faster than the current data annotation company.
No middlemen, complete automation, access to a global, 24x7 workforce, more control over the project status, build by developers for developers, ByteBridge.io's data training tool enables ML companies and developers to get projects done efficiently with its high-quality data collection and annotation services.
ML industry is transforming today’s tech development, and there’s no sign of this trend slowing down in the near future. ByteBridge.io is determined to empower the ML industry with the automated data service platform.
