Artificial Intelligence (AI) is one of the core parts of the 21st-century era of innovation. It is persistently progressing and becoming a fundamental part of all industries. Businesses are welcoming the transformative potential of AI technology to make data-driven decisions regarding businesses.
Though AI technology is still emerging and holds the unknown potential to alter today’s industries, it is still already used while developing modern solutions. AI revolution includes learning, reasoning, and perception from the available dataset. It consists of training large datasets, one of the biggest struggles of today’s organizations.
Data labeling and data management services play an important role in helping organizations train data for their AI models.
Data management
Data has become a crucial parameter in digital solutions providing data-driven analysis and predictions. Data management includes the practice of data collection, organization, protection, research, and storage. The potential of data management solutions to enhance the accuracy and performance of data queries increases when embedded with AI.
The AI workflow includes data identification, data cleansing, data aggregation, data annotation, data augmentation, Machine Learning (ML) algorithm development, ML model training, ML model tuning, and ML operationalization. Therefore, data management is one of the most important processes in AI workflow.
Data management services include data governance, data architecture, data integration, data quality management, data warehousing, data analysis, reporting, data security, data migration, and data backup.
Data annotation behind AI breakthroughs
Data annotation is one of the essential processes in data management and AI workflow as it helps label and categorize data for specific applications. A number of startups in silicon valley and around the world started data annotation services to help organizations improve AI workflow and performance and accuracy of ML training models.
Three decades ago, computer vision systems could not even recognize hand-written digits properly. But, today’s AI-powered systems with in-built advanced ML algorithms and powerful computation resources play a critical role in data labeling.
As a result, the increased demands for labeled data have automatically increased the employment and third-party companies providing annotated data to their clients. Data annotation services include labeling and categorizing the available data in various forms such as text, images, audio, and video.
To enhance the quality of AI applications, AI models need to act like humans requiring a high volume of training and human-powered data annotation. In addition, it leads to enhanced customer experience like relevant search engine results, speech recognition, computer vision, chatbots, product recommendations, and so on.
Thus, data annotation services are instrumental in fuelling AI breakthroughs in the modern world.
Advantages of data annotation:
- Data annotation helps in the supervised learning process of ML algorithms for precise data prediction. Annotated data is used in ML model training to learn different factors assisting the model in using available datasets and providing the most suitable decisions in different situations.
- ML-based AI models provide a seamless experience for end-users helping in enhancing the overall user experience.
The impact of data annotation services
Apart from its importance, data recently hit the headlines when data annotation startup Scale AI successfully raised 100 million United States Dollars (USD) funding.
Similarly, various data labeling startups contribute to the billion-dollar business behind AI, such as Mighty AI, Waymo, Argo AI, and Lyft. These startups offer domain-specific labeled data that focuses on quality control.
A continuous effort in developing advanced AI algorithms to help speed up manual annotation is increasing their demand in the marketplace. Self-driving and automated systems are one of the building blocks of modern infrastructures. Companies with such infrastructures are ready to pay millions of dollars for reliable data annotation services.
The complexity of data annotation increases with a large volume of data collected in various forms in a particular domain. For example, the image classification dataset labeled ‘cats’ is much easier than data labeling for autonomous vehicles. It includes pixel-wise semantic annotation, 3D semantic annotation, pixel-wise object instance annotation, fine-grained road segmentation, moving object trajectory, high-precision GPS/IMO information, and so on.
Regardless, domain-specific data labeling plays a crucial role in data annotation, making data annotation companies important in AI breakthroughs.
Conclusion
Data annotation services are advancing with emerging technologies that are still expensive and under experimentation. Data labeling startups have to keep themselves updated and upgraded in a competitive edge to provide precisely labeled data helpful for their clients for their application.
The AI era has just started, and it has a long way to go. Data annotation is the heart of AI breakthroughs in the future, and thus it will continue to be a billion-dollar business in the coming years.
