The demand for data annotation specialists has gone up with the rise in language models, training techniques, AI tools, etc. Data annotation– a critical step in supervised learning–is the process of labelling data to teach the AI and ML models to recognise specific data types to produce relevant output. Data annotation has applications in diverse sectors ranging from chatbot companies, finance, medicine to government and space programs.
The market for AI and ML data labelling has seen exponential growth of late. According to market research firm Cognilytica, the data labelling market will grow from USD 1.5 billion in 2019 t0 USD 3.5 billion in 2024.
Figure: Graph of Data Labelling market size in Asia-Pacific, by grandviewresearch.com
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Types of data labelling
A model is as good as the data it’s fed. Hence, it is imperative to ensure data quality of the highest grade with accurate labelling to optimise AI/ML models.
Let us delve into the types of data annotations:
- Visual data annotation
Visual data annotation analysts facilitate the training of AI/ML models by labelling images, identifying key points or pixels with precise tags in a format the system understands. Data vision analysts use bounding boxes in a specific section of an image or a frame to recognise a particular trait/object within an image and label it.
According to koombea.com, the key skills required for visual data annotation include:
- 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
- Knowledge of tools like OpenCV, Keras, etc.
- Audio data annotation
Audio data labelling has applications in natural language processing (NLP), transcription and conversational commerce. Virtual assistants like Alexa and Siri respond to verbal stimuli in real-time: Their underpinning models are trained on large labelled datasets of vocal commands to generate apt responses. Startups like Shaip are providing auditory data annotation services to tech giants like Amazon Web Services, Microsoft and Google.
The skills required for this field are:
- Spectrogram analysis
- In-depth knowledge of ML libraries
- Programming Languages like Python, Java, C++, etc.
- Auditory Database Management
- Knowledge of tools like Audacity, Adobe Audition, Cubase, Studio one, etc.
- Text data annotation
A major part of communications worldwide, be it business, art, politics or leisure, relies on the written word. However, AI systems have trouble parsing unstructured text data. Training the AI systems with right datasets to interpret written language enables the machines to classify text in images, videos, PDFs and files as well as the context within the words. One of the important applications of text data annotation is in chatbots and virtual assistants.
The key skills required for this field are:
- Knowledge in computational linguistics
- Experience in machine learning
- Database management
- Programming languages like Python, Java, C++, etc.
- Knowledge of tools like GATE, Apache UIMA, AGTK, NLTK, etc.
Emerging field with high salaries
The AI and data analytics industry is booming in India, and as a result the demand for data engineers, data analysts, data labellers and data scientists are exploding. Data annotation specialists should be adept in various skillsets ranging from machine learning to knowledge of tools specific to the type of annotations. The job demands long periods of focus, attention to detail, and ability to handle different aspects of the machine training process.
The freshers in the field of data annotation can expect packages ranging from INR 1.1 lakhs to INR 3 lakhs per annum.
Figure: Average starter payscale for data annotation from ambitionbox
According to a survey by Glassdoor, multinational corporations like Siemens, Apple, Google, etc., offer up to INR 7-8 lakhs/annum packages based on the skills and experience of the individuals.
Labelled data of high quality is the primary requirement for the smooth operation of any AI model. Hence, the demand for the implementation of a secure and cost-effective method of data labelling is of paramount importance now.
The emerging names in the business of data labelling services are:
- Acclivis technologies: Founded in 2009, this Pune based company provides high-end services in machine vision, deep learning, artificial intelligence & IoT. The job profiles the company is currently looking for include ML engineer, Image processing engineer, etc.
- Zuru.ai: The AI-powered data labelling company, founded in 2019, offers high-quality training datasets at scale.
- Cogito Tech: Founded in 2011 by Rohan Agarwal, this UP-based company offers data labelling services through its platform-agnostic strategy across sectors such as healthcare, automotive, agriculture, defence, etc.
- IMerit: Founded in 2011, this company extends end-to-end, high-quality data labelling across NLP, computer vision and various content services. The job profiles the company is currently seeking are – ML engineer, ITES executive, etc. IMerit’s control centre is in West Bengal.
- Wisepl: Founded in 2020 and based out of Kerala, this company applies different labelling techniques like Semantic Segmentation, KeyPoint Annotation, Polygon Annotation, Cuboid, Polylines Annotation, etc. Professionals interested in the field of data annotation can apply on Wisepl’s website.
With international conglomerates outsourcing AI-based services, India has become one of the leading names in the data labelling market globally.
