Data annotation is one of the most crucial processes in the AI world. It makes the set of training data available for machine learning algorithms. A computer vision-based AI model needs annotated images to make the various objects recognizable for a better understanding of the surroundings.
The data annotation process involves collecting data, labeling it, performing quality checks, and validating it, which makes the raw data usable for machine learning training. For supervised machine learning projects, it is not possible to train the AI model without labeled data.
During the whole process, well-trained human power with the right tools and techniques annotates data as per the requirements and then processes it in a highly secure environment for clients. The data is encrypted to ensure it can be safely delivered to clients to avoid any risk. So, right here, we will discuss the data labeling process step-wise facts.
DATA LABELING PROCESS
Collection of Datasets
The first step towards data annotation is understanding the problem to provide precise training data. Hence, collecting the datasets from the client is an important aspect. So, the raw data is collected directly from the client in a well-organized format.
Data is collected through a proper channel to ensure its originality and security. Many business enterprises follow different routes to send the data for labeling. Sometimes, it is supplied in an encrypted format, and after data annotation, it is again sent to the client in a secured format.
Labeling of Dataset
After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for supervised machine learning, labeled data is required, and proper labeling is important to make sure the AI model gets trained precisely and works in the right manner.
Choosing the right tools and techniques is another factor for data labeling. Image annotation is done to create the training data sets for a computer vision-based AI model. Quality also needs to be ensured to make sure the model can predict accurately. To consider all these points, two points also need to be discussed here—how to label data and who will label the data.
How to Label Data: After getting the data set for labeling, the annotation team has to decide the type of annotation applied here, like detecting, classifying, and segmenting the object. Here, if the client provides the specific tool or software, then annotators use it to annotate the images using the same.
Once the data sets are assigned to annotators, they are instructed on what type of annotation and what tools will best suit the task.
Who Will Label the Data? Similarly, the next step in the data labeling process is who will annotate or label the data. Two options are available for AI companies: first, they can organize an in-house data labeling facility, which could be easy to control and might cost less, but it can take extraordinary time due to the collection and labeling of entire data sets.
The second option is to outsource the labeling task to other data annotation companies, which have a team of well-trained and experienced annotators to label the data for machine learning with better efficiency and quality. The best part of outsourcing is that data can be aggregated quickly. On the other hand, transparency, accuracy, and high cost are factors that concern outsourcing services.
Quality Check and Evaluation
One of the most important factors of the data labeling process is checking the data’s quality after annotating it. Here, a qualified annotator manually checks the quality of each annotated image to ensure that the machine-learning algorithm is trained with the right accuracy.
Here, the data sets are also evaluated to validate them, and if there is any correction, the data is annotated correctly and finally validated for machine learning training. Highly experienced annotators are required to prudently check the quality of data labeled to make sure AI companies get the best high-quality datasets at the best pricing.
Final Delivery of Annotated Datasets
The last step in data annotation process is after labeling, the data need to be safety delivered to client. Here again, the authenticity and privacy of data are ensured till the data is delivered to the client. The mode of delivering the data also depends on the company to the company, but there should be a safe mode to send such data with complete confidentiality and safety.
Data Labeling Process at Cogito
Most companies follow the above-mentioned data labeling process, but few companies have a more complex or even more sophisticated but secured data annotation process. Cogito is one of the companies providing a world-class data labeling solution with the next level of accuracy. It follows international standards for data security and privacy to ensure the originality of the AI model.
