As a research scholar, you face this dilemma at one point or another: the stipend you receive (if at all you receive one at all) just isn’t enough. You generally don’t have a lot of time to contribute to an outside job but you need the money, so it’s key to find one with the highest possible hourly rate. The data annotation field is one such option that the research scholars can look into making that extra income easily while continuing their research with full focus. This field demands less mental work but a little mechanical task to earn.
With the development of language models, training methods, AI tools, etc., there has been an increase in the demand for experts in data annotation. An important stage in supervised machine learning is data annotation, which is the process of labelling data to educate AI and ML models on how to recognise particular data categories and produce pertinent output. Applications for data annotation can be found in a wide range of industries, including chatbot firms, finance, medical, government, and space missions.
The market for labelling data using AI and ML has recently shown exponential growth. The data labelling industry will increase from USD 1.67 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 25.1% from 2022 to 2030 predicts market research firm Region, and Segment Forecasts, 2022 – 2030.
Various data labelling types
The quality of a model depends on the data it is fed. To optimise AI/ML models, it is crucial to provide the highest possible data quality together with precise labelling.
Let’s examine the different categories of data annotations:
1. Data annotation in visual form
Image annotation is the process of assigning labels to digital photographs, usually requiring human input but occasionally requiring machine assistance. In order to teach the computer vision model about the items in the image, labels are predetermined by a machine learning engineer.
The following are the key competencies necessary for visual data annotation:
Analytical mathematics; in-depth knowledge of ML libraries; programming languages like Python, Java, C++, etc.; image analysis algorithms; visual database management; understanding of dataflow programming; and familiarity with tools like OpenCV, Keras, etc.
2. Annotations for Audio Data
Natural language processing (NLP), transcription, and conversational commerce all use audio data labelling. Real-time responses to spoken inputs are provided by virtual assistants like Siri and Alexa: To produce appropriate responses, their underlying models are trained using massive vocal command datasets that have been labelled. Tech behemoths like Amazon Web Services, Microsoft, and Google are using services from startups like Shaip to annotate audio files.
The abilities needed in this field are:
Analysis of spectrograms; Thorough familiarity with ML libraries; Python, Java, C++, and more programming languages; Management of the Auditory Database; Expertise with programmes like Studio One, Audacity, Adobe Audition, and Cubase.
3. Annotation for Text
The written word is a key component of communication on a global scale, whether it be in business, the arts, politics, or pleasure. However, unstructured text data is difficult for AI systems to parse. The ability to classify text in photos, videos, PDFs, and files as well as the context inside the words is made possible by training AI systems with the appropriate datasets to understand written language. Chatbots and virtual assistants are two significant contexts for text data annotation.
The main competencies needed in this profession are:
Knowledge of computational linguistics; experience with machine learning; database management; proficiency with programming languages such as Python, Java, and C++; and familiarity with tools such as GATE, Apache UIMA, AGTK, NLTK, and others.
A Sector with Strong Growth Prospects
The demand for data engineers, data analysts, data labellers, and data scientists is skyrocketing as a result of India’s burgeoning AI and data analytics industries. Specialists in data annotation should be skilled in a variety of areas, including machine learning and understanding technologies tailored to the type of annotations. Long periods of concentration, attention to detail, and the capacity to manage many components of the machine learning process are requirements of the job.
As per ziprecruiter.com, the average annual pay for a Data Labeling Job in the US is $50602 a year. Glassdoor poll found that depending on the employees’ talents and experience, major firms like Siemens, Apple, Google, and others give packages of up to INR 7-8 lakhs per year.
The basic need for the efficient operation of any AI model is labelled data of high quality. Therefore, it is crucial that a secure and economical technique of data labelling be implemented right away.
The following are a few new names in the data labelling services industry:
Founded: 06 November 2020
2D Bounding box, Cuboid 3D Bounding box, Semantic Segmentation, Polygonal Annotation, Lines and Splines, Image Background Removal.
Automotive, Fashion and Healthcare.
Headquarters: Levittown, NY
Image Annotation, Video Annotation, Text Annotation, Polygon Annotation, Polylines Annotation, Semantic Segmentation, Bounding Box, Landmark Annotation, 3D Cuboid AnnotatioN, 3D Point Cloud Annotation, Content Moderation.
Self Driving, Health Care, AI in Retail, Autonomous Flying, Robotics, Security, Satellite imagery, Agriculture.
You can visit my previous blog post on TOP 12 COMPANIES FOR RESEARCH DATA LABELLING for more details.