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