An audio, video, image or text turns out to be the training data for ML with the help of data annotation through technology and people. To build an ML or an AI model that performs the role of a human, needs higher capacities of training data. If a model needs to decide and execute, it needs to be skilled to comprehend particular information through data annotation. Data annotation is the process that helps to categorize and label the data for AI applications. Training data must be streamlined and annotated for a particular use case. Companies can establish and enhance the implementations of AI with the help of human-powered and superior quality data annotation.
The outcome is improved customer experience resolution that includes relevant search engine outcomes, product references, chatbots, speech recognition, computer vision and so on.
With main types of data like video, image, audio and text, most companies are making the best use of them to their advantage. As per the State of AI and ML report in 2020, companies said they used 25 per cent more data types than the previous year. Thus, with numerous workspaces and industries associated with distinct types of data, there is a huge requirement to maximise investment in trustworthy training data.