The accuracy of data annotation plays a crucial role in the successful and scalable deployment of machine learning activity. However, annotation is a monotonous manual job prone to errors, which needs a framework to ensure adherence to quality goals, controllability, and the alignment of control limits within specification limits.

In this free webinar, Sasken’s Ananda Jana presents a structured approach to achieving quality assurance in data annotation.

Key topics and takeaways:

• Learn about accuracy expectation levels from real-life data annotation projects
• Discover approaches for in-process quality monitoring
• Understand the factors that impact accuracy
• Learn how to improve data annotation accuracy to meet specification limits