Machine learning success depends on the human workforce.
“The global data collection and labeling market size was valued at USD 1.0 billion in 2019 and is expected to witness a CAGR of 26.0% from 2020 to 2027,” quote from a market analysis report by grand view research. At present, the application scenarios of artificial intelligence are constantly extended.
Behind the rapid growth of the AI industry, the new profession of data annotator is also expanding. There is a popular saying in the data annotation industry, “more intelligent, more labors”. The human workforce plays an important role in the data annotation industry.
These annotation labelers could work at home as freelancers. They can be trained to categorize and annotate data on various platforms, labeling companies such as Cloudfactory, Labelbox, allow work remotely.
As labor cost takes up the most part of annotation service, most of the data companies follow similar principles such as outsourcing to countries with a cheaper labor cost.
Different data types require different skill sets. Some require professional background. For medical data, the image segmentation and tumor areas annotation needs to be completed by annotators who have a medical background.
- Cost reducing: With the help of AI-assisted capabilities, clients can save more money as the labor cost goes down.
- Time reducing: Make the large-scale requirement of training data done in a short time. Using AI-assisted tool can improve efficiency multiple times
Can we get rid of the human workforce?
The answer is no.
In fact, manually labeled data is less prone to errors. The human workforce cannot be replaced by some tools leading with an AI-based automation feature, especially dealing with exceptions, edge cases, complex data labeling scenarios, etc.
In conclusion, the human workforce cannot be replaced by some tools leading with an AI-based automation feature, regarding quality assurance and data exception.
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ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
All work results are completely screened and inspected by the human workforce.
Moreover, the real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
While dealing with complex tasks, the task is automatically transformed into tiny components to maximize the quality level as well as maintain consistency, further reducing human error.
On ByteBridge’s dashboard, developers can define and start the data labeling projects and get the results back instantly. Clients can set labeling rules directly on the dashboard.
In addition, clients can 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.
As a fully-managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to get involved in the QC process.
“High-quality data is the fuel that keeps the AI engine running smoothly. The more accurate annotation is, the better algorithm performance will be” said Brian Cheong, founder, and CEO of ByteBridge.
Designed to empower AI and ML industry, ByteBridge promises to usher in a new era for data labeling and accelerates the advent of the smart AI future.