What is data labeling? Criteria to choose a data labeling service
Data labeling is an important step in developing Machine Learning models. Although data labeling seems to be a simple task, it is not easy to do and costs a lot of time and labor work.
Consequently, many businesses choose outsourcing data labeling services to better focus their resources on core business activities. GPBPO will give you an insight into the notion of data labeling services and how to choose the best data labeling service partner for your business.
1. What is data labeling?
Data labeling service, also known as data annotation, is the process of adding tags to data (image, text, audio, etc.) for data description and to help computers to process or “understand” that data.
Properly labeled data is essential for training AI algorithms and Machine Learning so that they can understand how a part of data is connected with the next one.
For example: labels can indicate whether a picture has a bird or a car, what words contain in a recording, or whether a tumor appears in an X-ray picture. Data labeling is used for a variety of purposes, including computer vision, natural language processing, and voice recognition.
2. Why is data label important for business
According to many studies, the pandemic has accelerated AI application by 32%, therefore, data labeling is becoming more important than ever. Businesses use AI technology to automate decision-making processes and exploit new business opportunities. Data labeling allows machines to understand the physical world and opens up opportunities for many types of businesses and industries.
For example: higher quality of properly labeled data gives your company an edge over other competitors in fields related to Machine Learning.
3. How to choose a data labeling service provider
Understand your business goals
Businesses need to keep in mind what goals they want to achieve through their Machine Learning models before choosing a data labeling service provider.
Different service providers meet different requirements whereby some may only implement text-based data labeling while others may provide data labeling services from audio, video and images.
Businesses may also experience the issue that providers do not deliver projects on time, resulting in slower workflows or lower quality data.
Security
When working with a data labeling service provider, a business may be sharing its data, which is considered personal or sensitive, with that provider. For example: you may be sharing financial or medical data. Therefore, before making a decision, you should consider how the provider ensures the security of your data when it is in their possession.
As a next step, businesses should also ensure that all members of that data labeling entity are willing to sign the NDA – Non-Disclosure Agreement before they deploy any labeling activities.
Feasibility assessment
DYour business may have found a number of potential providers that could meet your needs. At this stage, you should ask for proof of feasibility before entering into a contract with any provider. Even if you only have one suitable provider, this will help assess the provider’s ability to meet your needs and fulfill your goals.
By providing the labeling entities with a small portion of your data to label, you can also respond to adjust if necessary before you start your data labeling project.
Provider’s workflow
Businesses may also prefer to choose a provider who has experience in the same field as your business. They should also learn more about the methods and procedures that your provider applies such as quality control throughout project implementation.
Project size
Before choosing a provider, you should consider not only the current size of your data, but also the size as you scale. By the time your Machine Learning model gradually improves, you will need higher volumes of quality data. The provider you choose must be able to scale accordingly. Therefore, the labeling service provider must be flexible enough to adapt to changes in the volume of labeled data and the complexity of the project.
Data quality assessment
The quality of the data provided plays an important role in Machine Learning models. The accuracy of labeled data should be taken into consideration because low quality data will result in the first error occurring when training your model and the second error occurring when the model uses those low quality data to make future decisions.
Therefore, you should pay more attention to the way providers work and their QA methods so that you can find the most suitable labeling service provider for your project.
4. Why to choose data labeling service at GPBPO?
- 16 years of experience in providing data labeling services for machine learning and artificial intelligence projects.
- Processing multilingual projects, up to 80 languages, including rare languages.
- The staff ensure to meet the project requirements according to the size required by clients.
- Being partner of large domestic and foreign technology businesses such as: RWS, Viettel, FPT.
- Signing NDA agreement and ensuring 100% clients’ data security.
To learn more about data labeling solutions for your business, please contact Hotline: 0926 021 999 for immediate advice.
