Artificial intelligence (AI) and machine learning are here to stay. They have changed the way we live and experience the world around us. These technologies throw up some amazing opportunities that can help drive the global economy. Be it music, banking, or medical care, you will find machine learning and algorithms powering the latest innovations.
Businesses are embracing Machine Learning (ML) and Artificial Intelligence (AI) for text and image annotation because of the accuracy, speed and comprehensiveness these services provide. In addition, these services eliminate the risks associated with managing data diversity, reducing bias and scaling. The annotation process begins with marking up a dataset and its characteristics with a
Both automation and human factors play a crucial role in the success of any data labeling or annotation projects. The groundwork involved in building these projects is time-consuming, complex and expensive. To a large extent, the success of any such projects depends on data scientists, data engineers and data modelers. In fact they are the
Companies that rely on internal teams or automation to annotate data often find it difficult to manage increasing workloads and yet assure the same quality, speed, and security. Automation is the first casualty in such instances. Why? A lot of time is spent on perfecting algorithmic models to accurately match complex behavioral patterns and make
The data annotation market is expected to grow at 25.6% for the next five years. The adoption of AI-based services in different domains has contributed to this rise in demand. Many sectors such as healthcare, automobiles, telecom, and e-commerce among others are finding it expedient to collect datasets from different sources and label them based