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Standard data labeling expertise is not enough

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AI solutions combined with technological and human ingenuity lead to faster, more accurate outcomes

The rate at which organizations are developing AI solutions is growing exponentially. So is the need for high-quality training data. Gartner research has found that there’s an urgency of leveraging AI for business transformation, but 50% of IT leaders struggle to move their AI projects past proof of concept – one reason being a lack of data necessary to train AI solutions.

This paper discusses the challenges of data labeling, and how organizations need to pursue the best – rather than just the fastest – data-labeling service that takes into consideration quality, flexibility, scalability, and cost of data labeling.

About authors

Marek Sowa

Head of Intelligent Automation Offering & Innovation, Capgemini’s Business Services
Marek Sowa is head of Capgemini’s Intelligent Automation Offering & Innovation focused on adopting AI technologies into business services. He leverages the potential hidden in deep and machine learning to increase the speed, accuracy, and automation of processes. This helps clients to transform their business operations leveraging the combined power of AI and RPA to create working solutions that deliver real business value.

Vijay Bansal

Director – Global Head – Data Labeling Services, Capgemini Business Services
Vijay has extensive experience working in map production, geo-spatial data production, management, data labeling and annotation, and validation roles. In these positions, he aids machine learning and technical support initiatives for Sales teams, coordinates between clients, and leads project teams in a back-office capacity.

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