High-quality data is built on the foundation of good AI algorithms. As compared to the complex math associated with creating such algorithms, AI is mainly dependent on data.
Without reliable, accurate, and complete datasets, AI systems lose their potential. Therefore, there’s a heavy emphasis on the importance of data labeling, where we identify raw data and add meaningful labels, providing context that helps machine learning models to learn from it.
You’ll find many use cases that require data labeling, including natural language processing, computer vision, and speech recognition.
What is data labeling?
Data labeling is a type of machine learning model where we identify objects in raw data (videos, images, audio, or text). Later, we add them with labels, helping machine learning models to ensure accurate estimations and predictions. To use machine learning and solve tangible business problems, data labeling has become an important requirement for many organizations.
As compared to traditional data labeling processes that are time-consuming and expensive, using machine learning models can label data seamlessly.
Overview of the data labeling process
The data labeling process requires numerous steps, each ensuring the development of high-quality labeled data.
First, we start with data collection. This is where data scientists get raw data from different sources. Next, we look into data preprocessing. Here, we format and clean the data to prepare it for labeling, ensuring that the data is in a suitable condition for accurate annotation.
Moving forward, labelers assign accurate and relevant labels to the data, which is a valuable resource for training machine learning models. Next, we conduct quality assurance checks of the labeled data that offer consistent and reliable labels.
Approaches to data labeling
While working on a new AI project, the key decision to make is to learn how to approach the data labeling process effectively. Let’s look at what needs to be done.
1. Internal manual labeling
Internal manual labeling is one of the first techniques for creating training data for machine learning models.
This involves examining each data point by using subject-matter expertise for labeling. However, this approach has its challenges. You need to have the expertise to label the examples effectively. Moreover, companies require important financial resources for scaling this approach and developing adequate training sets for modern models.
2. External manual labeling
In external manual labeling, data teams need to break labeling tasks into small chunks and assign them to contractors or temp workers. Next, the workers apply labels as per the rules defined for the task.
Adding more workers or dismissing them after job completion will help data teams to enhance their data-generation capacity, removing the need to hire more employees. This reduces costs. However, outsourcing data labeling to third-party contractors will add a few challenges, including linear cost scaling, privacy concerns, lack of subject matter expertise, and poor data quality.
3. Semi-supervised labeling
Semi-supervised labeling uses the unlabeled data structure to complement the labeled data.
Data teams collect labelled data points for training two or more small models by using different model architectures. Later, these models are asked to predict the labels for the unlabeled data. Next, they apply the predicted labels when two or more models agree.
4. Automated data labeling
In automated data labeling, existing foundation models are used by data scientists to label data at scale with less human intervention. By using large language models, data scientists can use this approach for video, image, and audio classification tasks.
Best practices for data labeling
Some of the key practices for data labeling include the following.
1. Collect diverse data:
To minimize dataset bias, the data should be diverse. According to the characteristics of the data, bias can be prevented in different ways. If data is collected for NLP, you need to deal with assessment and measurement.
For example, by taking the number of arrest rates, you cannot attribute a higher possibility of crime in a country. Therefore, eliminating risks from collected data is a key step that precedes data annotation.
2. Set up an annotation guide
In today’s competitive AI and machine learning environment, composing clear, concise, and informative annotation guidelines is beneficial. Especially, annotation guidelines avoid mistakes before affecting the training data.
3. Keep communication open
Maintaining communication with managed data labeling teams can be challenging, particularly when the team operates remotely. This setup increases the likelihood of miscommunication and can leave key stakeholders uninformed.
To enhance productivity and project efficiency, it’s essential to establish a reliable and user-friendly communication channel with the team. Consider scheduling regular meetings and creating group channels to facilitate the quick exchange of vital insights.
4. Provide regular feedback
Share annotation errors in labeled data with your team to create a more efficient quality assurance (QA) process. Providing regular feedback enables them to better grasp the guidelines, leading to improved data labeling quality.
Ensure that your feedback aligns with the existing annotation guidelines. If you identify an error that isn’t addressed in the guidelines, think about updating them and communicating the changes to the team.
What to look for when choosing a data labeling platform?
Some of the key factors to look for while choosing a data labeling platform include
1. Inclusive tools
Before selecting a data labeling platform, evaluate the tools that are suitable for your specific use case. For instance, you might require a polygon tool for labeling cars or a rotating bounding box for labeling containers. It’s crucial to ensure that the platform you choose includes the necessary tools to produce the highest quality labels.
Additionally, think ahead and consider the labeling tools you may need in the future. Why spend time and resources on a labeling platform that may not be useful for upcoming projects? Training employees on a new platform can be both time-consuming and costly, so planning can help you avoid potential headaches.
2. Integrated management system
Effective management is fundamental to the success of any data labeling project. Therefore, it’s essential to choose a data labeling platform that includes an integrated management system for overseeing projects, data, and users.
A strong platform should allow project managers to monitor project progress and user productivity, communicate with annotators about mislabeled data, implement an annotation workflow, review and edit labels, and ensure quality assurance.
Robust project management features play a significant role in achieving equally powerful prediction results. When selecting a platform, keep an eye out for typical features found in successful project management systems, such as advanced filtering and real-time analytics.
3. Quality assurance process
The accuracy of your data is crucial for the quality of your machine learning model. Therefore, it’s important to select a labeling platform that includes a quality assurance process, allowing project managers to oversee the quality of the labeled data.
In addition to a robust quality assurance system, the data annotation services you choose should be well-trained, vetted, and professionally managed to ensure optimal performance.
4. Privacy and security
The privacy of your data should be your top priority. Select a secure labeling platform that you can rely on to protect your information. If your data is highly specialized, consider requesting a workforce experienced in handling your specific project requirements to minimize the risk of mislabeling or data leakage. Additionally, it’s wise to review the security standards and regulations that your chosen platform adheres to.
5. Technical support and documentation
Make sure the data annotation platform you select offers comprehensive technical support, including up-to-date documentation and an active support team to assist you throughout the data labeling process.
Technical issues can arise, so it’s important to have a support team available to resolve these problems quickly and minimize disruptions. Before committing to the platform, consider inquiring about how the support team handles troubleshooting assistance.
The role of data labeling in AI automation and digital transformation
Data labeling is a critical component of artificial intelligence (AI) and plays a significant role in driving automation and facilitating digital transformation across various industries.
Here’s how effective data labeling contributes to these advancements:
1. Improving machine learning models
- Quality training data: Data labeling provides the necessary training data that machine learning models require to learn and make accurate predictions. By labeling data correctly, organizations can build more robust AI systems that automate processes, analyze trends, and generate insights effectively.
- Improved accuracy: Well-labeled data leads to higher accuracy in AI models. When models are trained on accurately categorized data, they can better understand patterns and deliver reliable outputs, which is essential for automating tasks such as image recognition, natural language processing, and predictive analytics.
2. Streamlining processes
- Automation of repetitive tasks: AI systems powered by well-labeled data can automate repetitive and time-consuming tasks. For instance, in customer service, chatbots can efficiently handle inquiries if trained on labeled datasets that include various customer interactions. This automation not only saves time but also allows human employees to focus on more complex tasks that require critical thinking.
- Efficiency in operations: Data labeling helps organizations optimize their operations by enabling AI systems to analyze vast amounts of data quickly. For example, in supply chain management, labeled data can help predict demand, manage inventory, and streamline logistics, leading to more efficient operations and reduced costs.
3. Facilitating digital transformation
- Informed decision-making: With accurate data labeling, businesses can leverage AI insights to make informed decisions. This capability supports digital transformation initiatives by enabling organizations to adopt data-driven strategies that align with their goals and improve overall performance.
- Customization and personalization: Data labeling allows businesses to better understand their customers’ preferences and behaviors. By analyzing labeled data, organizations can create personalized experiences, such as tailored marketing campaigns or customized product recommendations, enhancing customer satisfaction and engagement.
4. Supporting innovation
- Rapid prototyping and development: Data labeling accelerates the development of AI applications by providing the essential data needed for prototyping. Organizations can quickly test new ideas and iterate on their products, fostering an environment of innovation.
- Cross-industry applications: As industries increasingly adopt AI technologies, data labeling becomes crucial in various sectors, from healthcare and finance to retail and manufacturing. This cross-industry application of AI contributes to a broader digital transformation, enabling organizations to stay competitive and adapt to changing market demands.
Almost every AI application begins with data labeling, and the decisions teams make about how to label their data can significantly affect the project’s return on investment.
Teams can explore various approaches, ranging from manual labeling done internally to programmatic labeling. The final decision will depend on factors like task complexity, budget, scale, and data privacy concerns.
No matter the use case or the organization’s level of machine learning maturity, having an effective data labeling strategy is crucial for the success of any machine learning project.
