Data annotation is considered fundamental for handling AI applications and complex ML tasks, such as autonomous driving, stock market forecasting, and more. The main task of data labeling is to select relevant labels for each piece of data, making raw and unstructured data a source of information for machine learning and training. So, how to do specific data labeling? Let’s introduce it below.
How to do data labeling?
Data annotation is the process of labeling data in various formats (such as video, image, or text) so that machines can understand it. For supervised machine learning, labeled datasets are critical because ML models need to understand input patterns to process them and generate accurate results.
1. Data collection
The collected data objects include data of various types and formats such as text, pictures, video and audio.
2. Data cleaning
The newly collected data is unstructured, and some data are incomplete, inconsistent, and noisy. Data cleaning is required to perform operations such as filtering, deduplication, checking for gaps and filling in gaps, smoothing noise, etc. to clean the data. into a format suitable for labeling to help obtain high-quality, high-precision training data.
3. Data annotation
After the data is cleaned, it can enter the core link of data labeling . In the actual labeling work, the data administrator will divide the data to be labeled into different data package tasks according to different requirements. Each data task will have different specifications and labeling form requirements, and then assign labeling tasks Labeling work for multiple labelers.
4. Data quality inspection
In order to improve the accuracy of data output, after the labeler completes the labeling work, the quality inspector needs to inspect the data, and the data that finally passes the quality inspection link is the data that can be used for machine training and learning.
What are the main challenges in data labeling?
1. The cost of labeling data
Data labeling is generally done manually, and labeling data requires a lot of manpower, and it is also necessary to maintain the quality of the data. Therefore, data labeling requires a lot of labor and management costs.
2. The accuracy of labeling
Human errors lead to poor data quality, and these errors directly affect the predictions of AI/ML models. Therefore, generating high-quality training data is another challenge for data labeling work. There are two main types of dataset quality – subjective and objective – and both of them can create data quality problems.
