Flexibility — Key Advantage in Data Annotation Market

 Data Annotation Market Size

The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to Research And Markets’s report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future.

The data annotation industry is driven by the increasing growth of the AI industry.

Data Annotation Process is Tough

Unlabeled raw data is around us everywhere, such as emails, documents, photos, presentation videos, and speech recordings. The majority of machine learning algorithms today need labeled data in order to learn and get trained by themselves. Data labeling is the process in which annotators manually tag various types of data such as text, video, images, audio via computers or smartphones. Once finished, the manually labeled dataset is fed into a machine-learning algorithm to train an AI model.

However, data annotation itself is a laborious and time-consuming process. There are two choices to do data labeling projects. One way is to do it in-house, which means the company builds or buys labeling tools and hires an in-house labeling team. The other way is to outsource the work to renowned data labeling companies like Appen, Lionbridge.

The booming data annotation market has also stimulated multiple novel players to secure a niche position in the competition. For example, Playment, a data labeling platform for AI, has teamed up with Ouster, a leading LiDAR sensors provider, known for the annotation and calibration of 3D imagery in 2018.

Flexibility is the Key Advantage in Data Labeling Loop

As the high-quality standard, data security, scalability are the most important measurements in labeling service, we may have a look at the rest competitive parts, for example, flexibility and customer service.

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.

Therefore, more engagement and control of the labeling loop for clients would be a key competitive advantage as it provides flexible solutions.

Solution

ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.

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 or data transfer. The platform also allows users to get involved in the QC process.

End

“High-quality data is the fuel that keeps the AI engine running smoothly and the machine learning community can’t get enough of it. 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.

Data Annotation Market sees tremendous growth in the forthcoming future

The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to ResearchAndMarkets’s report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future.

This is not a surprising trend. The rapid growth of data labeling industry can boil down to the rising integration of machine learning into various industries.

                   (source: statista)

Unlabeled raw data exits around us everywhere, for example, emails, document, photos, presentation videos and speech recordings. The majority of machine learning algorithms today need data labeled so as to learn and train themselves. Data labeling is the process in which annotators manually tag various types of data such as text, video, images, audio through computers or smartphones.Once finished, the manually labeled dataset would be packaged and fed into algorithms for machine learning's model training.

However, data annotation itself is a laborious and time-consuming process. There are two choices to do data labeling projects. One way is to do it in-house, which means the company builds or buys labeling tools and hires an in-house labeling team. The other is to outsource the work to renowned data labeling companies like Appen and LionBridge, who can handle product scale and guarantee quality.

The booming of data annotation market has also stimulated multiple novel players to secure a niche position in the competition. For example, Playment, a data labeling platform for AI, has teamed up with Ouster, a leading LiDAR sensors provider, for annotation and calibration of 3D imagery captured by its sensors in 2018.

ByteBridge.io, a data labeling platform, has innovated the industry through its robust tools for real-time workflow management. On ByteBridge’s platform, developers can define and start the data labeling projects and get the results back instantly. It not only improves the efficiency dramatically but also allows clients to customize their task based on their needs. 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 audit the data quality.

“High-quality data is the fuel that keeps the AI engine running smoothly and the machine learning community can’t get enough of it. The better the annotation, the more accurate the algorithm’s results become. Properly annotating data paves the way for efficient machine learning,” said Brian Cheong, founder and CEO of ByteBridge.io.

Designed to empower AI and ML industry, ByteBridge.io promises to usher in a new era for data labeling and accelerates the advent of the smart AI future.

Data labeling: a potential and problematic industry behind AI

Data labeling is not as mysterious as AI. To put it in a simple way, it applies multiple labeling tools to process data, the basic element of AI, so as to make data understandable for computer version and "teach" AI to identify, judge and act like human beings. If data serves like oil for AI, data labeling is to refine crude oil into gasoline.

At present, data labeling has been powering various industries such as autonomous driving, agriculture, healthcare, retail to turn them more efficient through the AI revolution.

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

According to Fractovia, data annotation tools market was valued at $650 million in 2019 and is projected to surpass $5 billion by 2026. Another report released by McKinsey in April 2017 estimates that the total market for AI applications may reach $127 billion by 2025. The expected market growth refers to the increasing demand of high-quality data labeling service for the AI industry development.

However, compared to the fancy high-tech AI, data labeling is labor-intensive in essence. Considering their great contributions to fueling AI industry, data labelers deserve more attention to improve their treatment and social status. The number of full-time data labelers in China has reached up to 100,000 and part-time labelers almost totaled 1 million. An ordinary data labeler in Baidu AI center labels 1,300 images and earns less than 25 dollars every day, which is much better than the cheaper labor force of small labeling teams in less developed counties and villages in China.

Data labeling industry has a low threshold for newcomers and it is more likely to be subcontracted by middlemen at all levels due to its huge amount, tight cost and schedule. Middlemen tend to lower the cost to seek higher profit. For a typical small label team with 20 staff, the labor cost is about $15-$ 25 per person a day. Unfortunately, such small label teams cannot guarantee the data quality and project delivery time due to various reasons such as incompetency, miscommunication, poor regulation and dysfunctional competition, which in turns wastes money and time for a couple AI companies.

"We are eager to find reliable and cost-effective data labeling teams. The accuracy and quality of the processed data determines the outcome of our machine learning training and final product," says Mr. Wang, a project manager in an AI company.

Bytebridge.io, a blockchain-driven data company, has also realized such urgent problems in the data labeling industry and committed itself to powering AI development through its automated data labeling platform.

Developers can create their data collection and labeling projects on Bytebridge's dashboard. The automated platform enables developers to customize various labeling projects and write down their specific requirements, upload raw dataset and control the labeling process for project management in a transparent and dynamic way. Developers can check the processed data, speed, estimated price and time without any limit of time and place.

In order to reduce data training time and cost when dealing with complicated tasks, Bytebridge.io has built up the consensus algorithm rules to optimize the labelling system: before task distribution, set a consensus index, such as 90%, for a task. If 90% of the labelling's results are basically the same, the system will consider they have reached a consensus. In this way, the platform can get a large amount of accurate data in a short time. If the machine learning model demands a higher accuracy of data annotation, for example, 99%, developers can use "multi-round consensus" to repeat tasks over again to improve the accuracy of final data delivery. Consensus algorithm mechanism can not only guarantee the data quality in an efficient way but also save budget through cutting out the middlemen and optimizing the work process with AI technology.

Bytebridge's easy-to-integrate API enables continuous feeding of high-quality data into machine learning system. Data can be processed 24/7 by the global partners, in-house experts through the distribution mechanism based on their education level, language capability and other parameters.

No middlemen, complete automation, access to a global, 24x7 workforce, more control over the project status, build by developers for developers, Bytebridge.io has cut off the intermediary costs and enabled AI companies to get projects done cost-effectively with its high-quality data services.

In the fierce market competition, only companies that focus on quality and service with their own complete and independent set of resources and technology could eventually survive. Bytebridge.io is one of such great companies in the data labeling industry and determined to accelerate the movement of AI revolution.

CONTACT:

contact: support@bytebridge.io
website: bytebridge.io