Managing Data Labeling Ops for Success with Audrey Smith - #583
AI Podcast#Data Labeling📝 Blog|Analyzed: Dec 29, 2025 07:41•
Published: Jul 18, 2022 17:18
•1 min read
•Practical AIAnalysis
This podcast episode from Practical AI focuses on the crucial topic of data labeling within the context of data-centric AI. It features Audrey Smith, COO of MLtwist, discussing the practical aspects of data labeling operations. The episode covers the organizational journey of starting data labeling, the considerations of in-house versus outsourced labeling, and the commitments needed for high-quality labels. It also delves into the operational aspects of organizations with significant labelops investments, the approach of in-house labeling teams, and ethical considerations for remote workforces. The episode promises a comprehensive overview of data labeling best practices.
Key Takeaways
- •The episode explores the practical aspects of data labeling for ML.
- •It covers the decision-making process between in-house and outsourced labeling.
- •Ethical considerations for remote labeling workforces are discussed.
Reference / Citation
View Original"We discuss how organizations that have made significant investments in labelops typically function, how someone working on an in-house labeling team approaches new projects, the ethical considerations that need to be taken for remote labeling workforces, and much more!"