Annotation Services

Data annotation services: Shaping the present and future of machine learning

Today’s digital economy is on overdrive, creating massive amounts of data. Some estimates suggest that by 2025, the world will be creating a staggering 463 Exabytes of data per day. Enterprises are turning towards artificial intelligence (AI) and machine learning (ML) solutions to exploit the power of this data. AI and ML have the potential to transform not just the economy, but the way we live and do business. From healthcare systems for detecting tumours and doing robotic surgery, autonomous cars to agriculture, there are countless applications of AI and ML. However, to reach the true economies of scale, organisations need to build solid digital assets that are the cornerstone of any AI/ML solution.

ML and deep learning solutions rely heavily on training data that is used to train ML models. Model performance is a key metric to track while building and measuring the effectiveness of AI/ML solutions. Apart from human and procedural errors, one of the main reasons for the failure of models is the lack of good quality training data. This is where data annotation comes into play. Data annotation involves creating labels and categorising data to create a foundational digital asset from which the ML algorithm can learn.


The importance of data annotation

Accurate data annotation serves as the foundation for high quality training data sets that are essential for ML algorithms and the associated AI applications. Data annotation provides the context for the ML algorithm to understand and interpret the data that it is being fed. Precise data labelling is necessary for the ML model to be able to understand the data item and establish relationships with other entities in the system. Human-in-the-loop (HITL) data annotation coupled with data curation and validation services ensures that ML models can be taken to scale with minimal risk of failure. The training data set is crucial to the success of AI and ML projects. Data annotation needs to be accurate and consistent to create reliable and scalable ML models that take less time to deploy. With fewer errors, such ML models prove to be cost and time efficient and give businesses the edge in a highly competitive and dynamic market. Organisations can opt for in-house data annotation or partner with an expert data annotation service provider that understands both the technology and the domain.


Factors driving data annotation

With an increasing demand for ML algorithms to be part of AI-powered solutions in industries such as security and surveillance, healthcare, automotive, and so on, there is an explosive demand for data annotation services. In fact the global data annotation and labelling market is expected to grow from USD 0.8 billion in 2022 to USD 3.6 billion by 2027, with a compound annual growth rate (CAGR) of 33.2 per cent.

  • Generative AI
  • Generative AI (GenAI) is expected to pivot several industries and is seeping into our lives whether we like it or not. According to a Forrester report, technology firms are including GenAI capabilities into their products and services. For instance, users can create posts on LinkedIn using the LinkedIn AI-generated content feature. Productivity and creative problem solving is expected to see a 50 per cent jump with enterprise AI initiatives. For example, tech companies and ad agencies are coming together to create brand-specific content for marketing and advertising using GenAI. While massive amounts of data are being generated, this data needs to be labelled and annotated for ML models to use. 

  • Natural language processing
  • Natural language processing (NLP) applications such as virtual chat assistants are on the rise and require well-annotated text and audio data for training.  Applications such as facial recognition or autonomous driving solutions require labelled data. Computer vision modelling is used by industries such as automotive and media/entertainment and requires accurate image and video annotations. In fact, several sectors require industry-specific annotations. For instance, medical image annotations require precise labelling of X-rays, CT scans and MRIs to train ML models for AI-powered healthcare applications.

  • Multi-modal AI
  • Multi-modal AI is considered the next big thing in GenAI. Multi-modal AI involves combining multiple data sources—images, speech and text—similar to how humans process information.  A combination of computer vision, NLP, speech processing and data mining enable AI systems to address complex real-world problems. For instance, in-vehicle assistance in cars can recognise voices and also record and interpret facial expressions of fatigue or tiredness to activate reminders to the driver. ML models for multi-modal AI require alignment of data labels of text, speech and images.

    Undoubtedly accurate and precise data annotation is the backbone for businesses to harness the true potential of AI and ML. In a data-driven world, businesses will require to leverage AI and ML to make informed decisions, find new streams of growth and stay ahead of competition.


How can Infosys BPM help?

The future of machine learning (ML) lies in accurate and reliable data annotation that enables ML models to interpret and understand data and the relationships between entities. At Infosys BPM, our platform plus our human-in-the loop Data Annotation Service enables efficient annotations for ML and helps clients build high-quality training data sets.