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 matters for machine learning, but how to acquire the right data？

Over the last few years, there has been a burst of excitement for AI-based applications through businesses, governments, and the academic community. For example, natural language processing (NLP) and image analysis where input values are high-dimensional and high-variance are areas that deep learning techniques are highly useful. AI has shifted from algorithms that rely on programmed rules and logic to machine learning where algorithms contain a few rules and ingest training data to learn and training themselves. "The current generations of AI is what we call machine learning (ML) — in the sense that we’re not just programming computers, but we’re training and teaching them with data,” said Michael Chui, Mckinsey global institute partner in a podcast speech.

AI feeds heavily on data. Andrew Ng, former AI head of Google and Baidu, states data is the rocket fuel needed to power the ML rocket ship. Andrew also mentions companies and organizations that are taking AI seriously are working hard to acquire the right and useful data they need. Supervised learning needs more data than other model types in machine learning area. In supervised learning, algorithms learn from labeled data. Data needs to be labeled and categorized for training models. When the number of parameters and the complexity of problems increases, the need of data volumes grows exponentially. 

Data limitations: the new bottlenecks in machine learning

An Alegion survey reported that nearly 8 out of 10 enterprises currently engaged in AI and ML projects have stalled. The same study also revealed that 81% of the respondents admit the process of training AI with data is more difficult than they expected. According to a 2019 report by O’Reilly, the issue of data ranks the second-highest on obstacles in AI adoption. Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, the teams management, etc. The data limitations in machine learning include but not limited to: 

• Data collection. Issues like inaccurate data, insufficient representatives, biased views, loopholes, and ambiguity in data affect ML’s decisions and precision. Let along the hard access to large volumes of high quality datasets for model development, especially during Covid-19 when data has not been available for some demanding AI enterprises.  

• Data quality. Low-quality labeled data can actually backfire twice: first during training model building and again when the model consumes the labeled data to make future decisions. For example, popular face datasets, such as the AT&T Database of Faces, contain primarily light-skinned male images, which leaves systems struggling to recognize dark-skinned and female faces. To create, validate, and maintain production for high-performing machine learning models, ML engineers  need to use trusted, reliable data.

• Data labeling. Since most machine learning algorithms use supervised approaches, data is useless for ML applications which rely on computer visions and supervised learning approaches, unless it is labelled properly. The new bottleneck in machine learning nowadays is not only about the collection of qualified data anymore, but also about the speed and accuracy of the labeling process.

Solution

ML needs vast amounts of labeled high-quality datasets for model training to arrive at accurate predictions. Labeling of training data is progressively one of the primary concerns in the implementation of machine learning algorithms. AI companies are eager to acquire high quality labeled datasets to match their AI model requirements. Researches are showing ByteBridge.io, a data collection and labeling platform that allows users to train state-of-the-art machine learning models without manual marking of any training data themselves. ByteBridge.io's dataset includes diverse and rich data such as texts, images, audios and videos with full coverage of languages, races and regions across the globe. Its integrated data platform eliminates the intermediate processes such as labor recruitment for human in the loop, test, verification and so forth.

Automated data training platform

ByteBridge.io takes full advantage of the platform's consensus mechanism algorithm which greatly improves the data labeling efficiency and gets a large amount of accurate data labeled in a short time. The Data Verification Engine, equipped with advanced AI algorithms and the highly trained project management dashboard has automated the annotation process which fulfills the needs and standards of AI companies in a flexible and effective way.

“We believe data collection and labeling is a crucial factor in establishing successful machine learning models. We are committed to building the most effective data training platform and helping companies take full advantage of AI's capabilities,” said Brian Cheong, CEO of ByteBridge.io. “We have streamlined data collection and labeling process to relieve machine learning engineers from data preparation. The vision behind ByteBridge.io is to enable engineers to focus on their ML projects and get the value out of data.”

Compared with competitors, ByteBridge.io has customized for its automation data labeling system thanks to the natural language processing (NLP) enabled software. Its Easy-to-integrate API enables continuous feeding of high quality data into a new application system.

Both the quality and quantity of data matters for the success of AI outcome. Designed to power AI and ML industry, ByteBridge.io promises to usher in a new era for data labeling and collection, and accelerates the advent of the smart AI future .

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.