Practical AI Production data labeling workflows
Sep 27, 2022
Mark Christensen, CEO of Xelex and a data labeling expert, discusses the intricacies of integrating data labeling into scalable workflows. He shares insights on managing team dynamics, emphasizing the need for clear instructions and tailored approaches to improve data quality. Mark highlights challenges faced in healthcare AI, particularly around data security and compliance, and advocates for robust training systems for annotators. Finally, he explores future opportunities in multilingual data labeling, shedding light on its potential to enhance customer experiences.
AI Snips
Chapters
Transcript
Episode notes
Data Labeling Challenges
- Data labeling accuracy is crucial, especially in specialized fields.
- In-house labeling is common for smaller projects, but larger projects often require a mix of in-house and external resources.
Invest in Annotators
- Avoid commoditizing annotators; invest in training and fair compensation.
- Building long-term relationships with annotators ensures data quality and consistency.
Annotator Training
- Train annotators thoroughly and ensure they understand the project's use case.
- Providing context and project details increases annotator buy-in and data quality.