Without further ado, let us first understand what exactly the meaning of Data Labelling is. It is basically the process of adding tags or labels to unprocessed data, such as images, video, audio & text, so that your machine learning model is able to identify or declassify the objects and their data sets or class which they belong to. It is by adding a meaningful and informative label to these particular data sets; it becomes easier for a machine to learn from it. Such professionally managed data labelling service help in creating high-quality AI training datasets. In any Artificial Intelligence (AI) platform, data labelling works in a way to acclimatize a computer or a machine to identify a certain class of objects, which can be in the form of images, videos and texts. It is to effectively power-up any machine learning initiative through AI training data that are labeled or tagged by human labellers. Such services are required for any computer vision model and natural language processing projects in emerging economies across the world. Some important tasks that come under data labelling are data annotation, data tagging, classification, moderation, transcription and processing.
It is one of the primary stages of data processing in a supervised environment. Human labellers that are working with datasets, reading, annotating or labelling them, must be extremely careful while processing them. Any mistake or inaccuracy can adversely affect the quality of any dataset and the overall performance of a computer/machine training model. In fact, data labelling is perhaps the most complex part of building a robust and a stable machine learning environment. Any wrongly or inaccurately labeled data can inflict severe consequences on the overall operation of a company. There are two types of human labelling service; Internal labelling & External labelling. Today, there are a variety of advanced tools and software that are used for data labelling services. If raw company data or training datasets are tagged flawlessly, a machine is able to identify each class of objects through its AI enabled programs, and thereby identify and make solid predictions.
Outsourcing Data labelling Services to Expert Agencies
Instead of hiring full-time or part-time employees to work on your data labelling project, it is always advisable to hire the services of experts in this exclusive or niche domain. If your company or firm is based out of the UK, you can try a specialist by the name if “Aya Data” that has vast project expertise in data labelling service, apart from data annotation, bounding box technology, data processing, computer vision and Natural Language Processing (NLP). One such certified ‘tech geek’ can be of great help in creating high quality Artificial Intelligence (AI) data for effectively and accurately training your company computer vision model. So, why employ a dedicated in-house team on your payroll, when you’ve the option of hiring the services of such professional data processing experts. It is not only cost-effective, but hassle-free too. In this way, your company or business can focus on its core deliverable area, and leave the task of data labelling or data annotation to specialists like these.
Who Requires Data Labelling Services?
Since data labelling is primarily used to help any machine learning model identify, recognize and understand certain objects, texts and images, it is critical in various high-end applications and industries. This particular service is mostly used in airport security where face recognition is required, by law enforcement agencies, in case of autonomous driving vehicles, in drone operation, robotics, by product advertising firms, core IT companies, healthcare industry, agricultural sector, by precision mapping & geo-tagging experts, and more. It is done by training a computer model through the process of bounding boxes that are drawn around any image, in order to properly identify and classify it. This is performed by human data labellers.
Different Data labelling Methods & Approaches
There are data labelling approaches and methods in this specialty domain that are used for accurately and precisely labelling raw data that would be further used for training computer vision models. It includes processes like In-House, Outsourcing, Crowdsourcing and Machine (ML) assisted data labelling. Here, ‘Inhouse’ refers to using your own company resources and manpower to label or tag data, Outsourcing refers to hiring freelancers or individual data experts to label your company data. Crowdsourcing is perhaps the most optimal and feasible way, as here you can hire a third-party data partner or technology expert that can offer its valuable expertise, insight, skills and know-how in labelling massive amounts of data that can be quickly deployed or used for your company’s operation. This is when you do not have in-house expertise or skills. Lastly, ML-assisted data labelling service is required when high-quality AI training data is needed on a large scale. This is basically performed by automating business processes that usually require high-end data categorization.
Utilizing Human-in-the-Loop
At the time of testing your data, humans are involved in the process. It is by using the Human-in-the-Loop technique; it helps in checking whether the computer vision model is able to identify the particular class of objects and is making accurate predictions. This human infused concept or element helps in identifying glaring gaps or loopholes in AI training data, provide feedback to the machine learning model and retrain the system, if it feels that a machine is making incorrect or inaccurate predictions. Human annotators are definitely needed to label AI-based training data that is to be fed into the machines. In a layman’s term, it refers to the constant supervision and validation of a machine’s AI based learning model by humans.
Conclusion
It can be said that a data labelling service is required by business entities as well as non-government organizations that want to effectively and seamlessly train their computer vision models for understanding and identifying different classes of objects in an accurate manner. The objects can be in the form of texts, images, videos and audios. It is by adding a label or a tag that helps a machine to identify what the data piece is all about. This particular service is also referred to as data annotation, tagging & classification for precisely identifying raw data for training any computer model of a company. It is all about adding tags to raw data and feeding a machine with such high-end AI data, so that it is able to accurately predict the objects and show the desired results.
