How to have high-quality data annotation for Machine Learning?
The role of data annotation
The development of AI has changed the way the world works, many industries apply AI in millions of projects. This requires a huge amount of training data; Machine Learning algorithms will learn through it and apply that knowledge to bring further useful predictions. Let’s find out the role of data annotation in this process and the methods to have high-quality data annotation.
We can understand simply that data annotation is the process of tagging data with a label for objects in collected data types such as images, videos, and text… Machine Learning algorithms will use labeled data to learn and recognize the objects. If AI companies train models with low-quality data, Machine Learning algorithms will not give valuable predictions. To understand more details about this, you can read: What is Data Annotation?
The fact that demand for data annotation is increasing, more and more vendors provide data annotation services in the market. To ensure the quality of data annotation projects and choose reputable vendors, we have to set strict requirements about data labeling quality. There are some of the best practices for data annotation quality.
Different ways to ensure high data annotation quality
Set up specific and comprehensive guidelines before starting projects
Before starting any project, we need to have a clear guide for teams to know what they need to do and avoid misunderstanding. The guidelines include detailed requirements:
- Accuracy rate: We need to give specific figures about the requirements of the accuracy rate, we also need detailed QA criteria to regularly compare and check the process.
- Qualification of the annotators: The data annotation job is not too difficult, just a very short training time that you can master basic data annotation skills. However, depending on each project and the specific requirements of industries and customers, the diverse level of annotators can meet that. You can combine a team with both high-level annotators with low-level annotators to ensure data annotation quality and save budget.
- Benchmarks of ideal output: Benchmark is the key factor for managers to visualize specific target results, from which managers will build steps to achieve success. This is also the basis for evaluating the performance of annotators and the quality of labeled data throughout the project, project managers can adjust flexibly if there are problems.
- Pilot project: Before deploying the project on a large scale, to avoid risks, a project pilot will be an essential step. The pilot project will help the team evaluate the actual time to complete the project, and the average performance of annotators, improve guidelines and set appropriate targets.
Apply QA process
Integrating a multi-layered QA process into the projects is an ideal way to meet the customer’s high standards across the globe. LTLTS GDS applies a multi-layered QA process including four steps: self-check, cross-review, vertical review, and final inspection.
- Self-check: Annotators need to check the tasks they have completed to detect errors; besides they need to self-assess their performance through the number of completed tasks and rework.
- Cross-review: When annotators perform a large amount of work over and over again, sometimes, it can be difficult for them to recognize their own systematic errors. Your colleague will help you discover your own mistakes.
- Vertical review: At this step, project managers will be responsible for checking the entire project and the work results of team members. In addition, project managers have a lot of experience who know the requirements of the project and help members understand the guidelines. Thus, the project manager is the right person who ensures the quality of annotated data in the project at this step.
- Final random inspection: Not all projects need to perform all 4 steps in the QA process, but for projects that require a high accuracy rate of up to 100%, final random inspection is a key step. The team that takes this step will randomly check 30% of the project work. In this step, the responsible team needs to compare with the latest updated feedback from the customer. A review cycle will help the data annotation project get the best results.
Provide regular feedback
During the implementation process, clients and vendors need to regularly review the work to find the best solution for potential problems. Besides, the fact that clients regularly provide feedback will avoid wasting time for reworking as well as help the team achieve the best results. All help to ensure the quality of the entire data annotation project.
Consider an experienced vendor
To be able to achieve success in any project, internal human resources are always the key factor determining the destination. We should look for vendors with experience in flexible human resource management. The team needs to regularly open periodic meetings and evaluations for the annotator. Besides training before each project, at the end of the project, the team needs to give feedback to all staff. Especially if the team has members working remotely, it is essential to exchange and discuss problems. Building strong and effective internal human resources will contribute to high-quality projects.
LTS GDS is proud of winning the prestigious 2021 Sao Khue Award for excellent Data Annotation services. With our professional team and multi-layered QA process, we offer high-quality data annotation services for customers around the world.