What is data labeling?
Computers themselves cannot process visual data in the same way as human brains do. The computer must be told what it is interpreting and given context. That’s where humans come in and train Artificial intelligence (AI) algorithms to perform intended tasks via data labeling. Data labeling is a process of humans taking in unstructured visual data like raw text, audio, images, and video in order to produce machine learning models that can recognize the meaning of this content. This process is inevitable because AI and machine learning models need to be trained consistently to become more efficient and effective in delivering required outputs.
Why data labeling matters
Businesses are implementing AI technology in order to automate decision-making and leverage new business prospects, but it is not as simple as it appears. Data labeling is considered one of the most difficult barriers to AI adoption in the industry. The quality of a machine learning project all comes down to how annotators can handle the process. Adding metadata tags to categorize thousands of data components is time-consuming and complex, and requiring a high level of accuracy. This is because faulty datasets can translate into wrong predictions by AI, hence, deliver a bad experience to customers.
Why outsourcing can help
Data labeling is essential, but also resource-heavy and time-consuming. Some businesses may have to decide which one is better for their project: performing data labeling in-house or outsource it? We do understand that labeling in-house has a few advantages. You can keep control and visibility over the data collection process and sometimes subject matter experts with the necessary knowledge may be already in-house. However, considering the time and workforce-heavy, outsourcing is an ideal approach for any data labeling company. It allows you to assemble a temporary well-trained team that can work on a project over a set period of time, focus on your main core business, and avoids seasonal employee expansion.
Here at SIBAI, we guarantee to provide you the best-in-class data labeling solutions at a fraction of cost without sacrificing quality. We understand each business is distinctive and has particular requirements; therefore, we are also striving to provide scalable data labeling solutions powering your growth.
With a team of over +200 in-house data labeling experts, you may tap into the knowledge of experienced annotators. We can flexibly form the exact workforce you require, no matter your project scale, and process hundreds of thousands of data rows in a fraction of minutes, ensuring that your models have the knowledge they require to function in the real world.
Four types of data labeling
Our high-performance team works toward addressing your entire training data model. We provide data labeling solutions by utilizing the association of human talent and advanced tools to make each data classified for machines or computer vision.
Our qualified data annotators will label, tag, and categorize images for input into machine learning software, making it easier to detect the varied objects just like human-being. Through the bounding boxes, polylines, landmarks, semantic segmentation, and more, we power your computer learning models to easily recognize and process labeled data. These labeled datasets could be used to guide self-driving cars or in facial recognition algorithms.
We can label any type of video using advanced tools to capture and detect all moving objects with frame-by-frame annotated data. Whether it is for computer vision learning, object tracking for self-driving cars, or human activity and gesture recognition, we can deliver an optimal solution with the highest quality assurance by using tools such as semantic segmentation, bounding boxes, or even custom tools that you want us to work on.
Labeling meaningful metadata to the original dataset is crucial to make the entire sentence understandable for a machine learning algorithm. Our annotation professionals with a background in natural language ensure to deliver the highest accuracy level using text highlight, polyline, or any custom tool.
Outsource your audio annotation requirements then our linguists will classify and add metadata to every piece of sound. We use the best-in-class annotation tool with high accurate tagging to make such sounds more comprehensible to voice-enabled applications.