What is Best Data Labeling Process to Create Training Data for AI?

Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings.

Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.

During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients. The data is encrypted to make sure it can be safely delver to the clients to avoid any risk. So, right here we will discuss about the data labeling process to step wise facts.


Collection of Datasets

The first step towards data annotation is understand the problem to provide the precise training data. Hence, collecting the datasets from client is an important aspect. So, the raw data is collected directly from the client in the well-organized format.

Also Read : How to Create Training Data for Machine Learning?

The data is collected through a proper channel to make sure its originality and security. Many business enterprises follow the different routes to send the data for labeling. Sometimes it is supplied in encrypted format and after data annotation it is again sent to client in the secured format.

Labeling of Dataset

After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for the supervised machine learning labeled data is required, and proper labeling is important to make sure AI model get trained precisely and work in the right manner.

Choosing the right tools and technique is another factor for data labeling. And in image annotation is done to create the training data sets for computer vision based AI model. The quality is also need to be ensured to make sure the model can predict with the accurate results. To consider all these points two points also need to 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 segmentation of the object. Here if client provides the specific tool or software, then annotators use to annotate the images using the same.

Once the data sets are assigned to annotators and instructed what type of annotation and what are the tools will be best suitable to annotate the data.

Who Will Label the Data: Similarly, the next step into data labeling process comes, who will annotate or label the data. Here, two options are available for the AI companies – first organize the in-house data labeling facility which could be easy control for you and might cost less but it can take extraordinary time due to collection and labeling of entire data sets.

The second option is outsource the labeling task to other data annotation companies, who have 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 data has the ability to aggregate quickly. While on the other hand transparency, accuracy and high-cost are the concerning factors with outsourcing services.

Quality Check and Evaluation

After annotating the data, checking the quality is one of the most important factors of data labeling process. Here, qualified annotator manually check the quality of each annotated images to make sure machine learning algorithm get trained with right accuracy.

Also Read : How to Build Training Data for Computer Vision?

Here, the data sets are also evaluated to validate the same, and if there is any correction the data is annotated correctly and finally validated for machine learning training. Here highly experienced, annotators are required to prudently the check the quality of data labeled to make sure AI companies get the best and high-quality datasets at 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 is ensured till the data is delivered to client. And the mode of delivering the data also depends on the company to company but there should be safe mode to send such data with complete confidentiality and safety.

Data Labeling Process at Cogito

Most of the companies follow the above discussed data labeling process but few companies have more complex or even more sophisticated but secured data annotation process. Cogito is one the companies providing the world-class data labeling solution with next level of accuracy. It is following the international standards for data security and privacy to ensure the originality of AI model.


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