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.

Brand New Data labeling service platform “ByteBridge.io” launched to better support machine learning industry

 Bytebridge.io, an automated data service provider to collect, manage, and process data sets for machine learning applications, was recently introduced into the AI industry.

It is a self-service platform to manage and monitor the overall data processing, specialized in data collection and labeling services for organizations, and to provide convenient toolkits for machine learning companies.

Bytebridge.io is backed by many world-class known investment companies, such as KIP, Union Partner, SoftBank Global Star Fund, and Ameba Capital.

Traditional data service providers use limited workers to finish multiple tasks at the same time, which would cause problems such as low-efficiency and long-waiting task delivery period to the clients. Bytebridge.io has come to a better solution. With close to million task partners across the globe, it supports workers in different regions to work at the same task at the same time on its platform, which allows works to work on tasks 24hr non-stop. It not only improves the efficiency dramatically but also allows clients to customize their task based on their needs by themselves.

Currently, Bytebridge.io has already been working with a few tech companies around the globe, helping them to build a machine learning system much faster through its automated data labeling process. With a handful of experiences, Bytebridge.io is confident that they could supply the best product and service to the AI industry.

Empowering data science developers to build a great machine learning product, Bytebridge is designed to build a strong data labeling infrastructure to the machine learning team with powerful automation, collaboration, and developer-friendly features.

“We are well-positioned to fuel the industrialization of machine learning across many sectors, we have a handful experiences in this industry and we understand the pain of developers are facing. Our goal is to relieve AI companies from the burden of machine learning data preparation and management and accelerate the machine learning development cycle, allowing them to build better AI in a shorter time,” said Brian Cheong, the Founder of Bytebridge.io.

About Bytebridge.io:

Bytebridge.io is an automated platform designed to accelerate the machine learning process It aims to power the machine learning industry with high-quality trained data.

How to Ensure Data Quality for Machine Learning and AI Projects

 Data quality is an assessment whether the quality of data is fit for the purpose. It’s agreed that data quality is paramount for machine learning (ML) and high-quality training data ensures more accurate algorithms, productivity, and efficiency for machine learning and AI projects.

Why is Data Quality Important?

The power of machine learning is dramatically due to its capability to learn on its own automatically after being fed with huge amount of specific data. In this case, ML systems need to be trained with a set of high-quality data, as poor qualify data would mislead the results.

In his article, “Data Quality in the era of Artificial Intelligence” George Krasadakis, Senior Program Manager at Microsoft, puts it this way:”Data-intensive projects have a single point of failure: data quality.” He mentions that because data quality plays an essential role, his team at Microsoft starts every project with a data quality assessment.

The data quality can be measured from 5 aspects:

* Accuracy: how accurate a dataset is by comparing it against a known, trustworthy reference dataset. Robots, drones, or vehicles rely on accurate data to achieve higher levels of autonomy.

* Consistency: data needs to be consistent when the same data is located in different storage areas

* Completeness: the data should not have missing values or data records

* Timeless: the data should be up to date

* Integrity: high integrity data comforts to the syntax (format, type, range) of its definition provided by data model

Achieving the Data Quality Required for Machine Learning

Traditionally, data quality control mechanisms are based on user experience and data management experts. It is costly and time-consuming since human labor and training time are required to detect, review and intervene in sheer volumes of data.

Bytebridge.io, a blockchain-driven data company, substitutes the traditional model by an innovative and precise consensus algorithm mechanism.

Bytebridge.io, the data training platform, provides high-quality services to collect and annotate different types of data such as text, image, audio and video to accelerate the development of machine learning industry.

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 80%, for a task. If 80% 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 customers demand a higher accuracy of data annotation, they 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 and the AI technology.

Conclusion

In his Harvard Business Review, “If Your Data Is Bad, Your Machine Learning Tools Are Useless,” Thomas C. Redman sums up the current data quality challenge in this way:“Increasingly complex problems demand not just more data, but more diverse, comprehensive data. And with this comes more quality problems.”

Data matters, and it will continue to do so; the same goes for good data quality. Built for developers by developers, Bytebridge.io is dedicated to empowering machine learning revolution through its high-quality data service.