In the rapidly progressing AI and Machine Learning landscape, the development and training of new models are gaining momentum. AI and ML systems are trained on data that is labelled and annotated by data labellers. Lately, the data labeller job is gaining prominence, and domain experts are being hired by companies like FlexiBench to annotate specialised datasets for client project requirements.
In this blog, we will discuss what exactly is data labelling and dive deep into the data labelling job, its requirements, challenges, and best practices.
Data labelling is the process of turning raw data into processed, annotated data sets that can be understood by machines and used for the training of AI models. This is done by identifying and adding suitable labels or tags to the data to specify what the data is about and point out individual elements within the data. This labelled data allows machine learning and AI models to make accurate predictions.
Four main kinds of data labelling include Image labelling, video labelling, text labelling, and audio labelling or annotation. Data labelling is the primary job of Data annotators and labellers and they are known as AI training specialists.
A data labeller’s primary responsibility is to annotate and label datasets, making them understandable and usable for training AI algorithms. Data labellers meticulously analyse and categorise data, identifying patterns, objects, or features within images, texts, or videos.
Data labellers contribute to the development and training of AI models, enabling machines to recognise and interpret information accurately. Therefore, they are called AI training specialists.
Data labelling is essential for AI and ML models because these systems are built from the ground up and are taught to interface with the human world and understand it via data. Raw and unannotated data makes no sense to such systems. What data labelling essentially does is point out, tag, and label individual elements, tones, and other characteristics of the data to make it understandable for the AI and ML systems.
Huge data sets that have been annotated and labelled allow new models to prepare reference points and organically learn new information through repetition and labelling.
Apart from computer proficiency and being detail-oriented, focused, and meticulous, there is no such minimal requirement for starting as a data labeller. The following are the broad qualifications for a data labelling job:
If you are in the job market for a data labeller job, there are two primary options available:
The future of the work of a Data Labeller in the AI era is filled with exciting opportunities. As AI advances, new opportunities for specialisation requirements will emerge across various industries. The integration of AI technologies necessitates a shift in skills and a focus on human-AI collaboration.
Individuals working as AI training specialists can position themselves for success in a world where this technology plays an increasingly prominent role.
In conclusion, data labellers are playing an extremely crucial role in the AI revolution as they produce the basic building blocks on which new AI systems are developed and trained. Their primary job is to add labels and annotations to make the data understandable for AI models.
Skills like computer literacy, attention to detail, language proficiency, and familiarity with labelling tools are important for this job. And as far as job opportunities are concerned, crowdsourcing platforms offer opportunities, but specialised platforms like FlexiBench provide additional benefits, allowing labellers to leverage their expertise in other domains to create specialised labelled data sets.
Data labelling jobs are showing promising prospects as AI advances, requiring specialised skills and integration with other traditional industries. Overall, data labellers contribute to the growth of AI and can position themselves for success in this evolving field.
Who is a data labeller?
A data labeller is a person who labels raw data sets, turning raw data into processed, annotated data sets that can be understood by machines and used for the training of AI models.
Why is data labelling important in machine learning?
Data labelling is important in machine learning because, with raw data making no sense to AI models, labelled and annotated data sets become essential for the training of AI and ML systems.
How can I find data labelling jobs?
Data labelling jobs are offered by multiple specialised and crowdsourcing platforms such as FlexiBench, Appen, Toloka, etc.
What are the career prospects in data labelling?
Data labelling jobs are showing promising prospects as AI advances, requiring specialised skills and integration with other traditional industries.