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While technology is growing exponentially, still many tasks are accomplished with human efforts. Such as moderating content, performing data duplication, data labeling etc.

A new workforce is emerging called data labelers thanks to AI, that’s set of people whose job is to go through huge datasets, consume the information and feed it back into the system. The data labeling service is mainly used to teach AI/ML algorithms and make them smarter and mature.

What is data labeling?

In simple words, giving a structured form to unstructured data is known as data labeling. Data like emails, social media, sensors etc. can be labeled in different and required manners like colored, highlights to mark the difference, similarities or types. This is so that when the data are fed into an algorithm for training an AI/ML (artificial intelligence / machine learning) system, the algorithm can rightfully identify the data and learn from it. When you trained an algorithm to understand signboard using images, data annotators or labelers will go through the dataset of images and mark or highlight sign board using annotation tools and feed this to an AI algorithm to make them more accurate and precise. The next time the algorithm encounters a signboard during a live drive through an area, it should be able to recognize the sign. The more images of signboards the algorithm is trained on, the better its accuracy and performance.

What we can easily find is, it is never 100% accurate. It is always an ongoing learning and improvement process. The automated tools can become effective enough to create good datasets.

Why Data Labeling?

Data Labeling is growing exponentially with the tremendous growth of Artificial Intelligence and Machine Learning activities in Enterprises Solutions. There are some handy tools like Amazon Mechanical Turk or Google’s AI Platform for Data Labeling. These are crowdsourcing marketplace, which helps to eliminate the cost and time required to complete the process of Data Labeling for ML. However quality and accuracy of processed data can be always questioned.  And such inaccurately labeled data can make your algorithms defective and inaccurate.

Machine learning models require lots of iteration and correction during development. It is a continuous and time-consuming process. The tolerance for errors will be very low while the solution is deployed for large enterprise and to be used by global teams. To train such AI and ML algorithms, one will need dedicated team for data labeling who can understand the algorithm and use cases and perform the data labeling with very high precision.

Not all platforms are sound enough to engage in high precision data labeling activities with human effort. Ushyaku Software Solutions helps to bridge that gap by offering high precision data labeling solutions at economical cost where our workforce will be accessible in any time zone in order to support our global clients. Such accurately processed data can make the ML algorithms more intelligent and mature so that the real goal of AI and ML can be achieved.

Contact us for any data labeling activities, we would be happy to help you with your inquiry. Apart from data labeling, Ushyaku Software Solutions offers offshore development services for Mobile and Web App Development, AI/ML Development, Application Support and on-site deployment of resources to global locations.

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