What is data labeling?

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This is a podcast episode titled, What is data labeling?. The summary for this episode is: This episode of Techsplainers explores data labeling, the critical preprocessing stage where raw data is assigned contextual tags to make it intelligible for machine learning models. We examine how this process combines software tools with human-in-the-loop participation to create the foundation for AI applications like computer vision and natural language processing. The podcast compares five distinct approaches to data labeling: internal labeling (using in-house experts), synthetic labeling (generating new data from existing datasets), programmatic labeling (automating the process through scripts), outsourcing (leveraging external specialists), and crowdsourcing (distributing micro-tasks across many contributors). We also discuss the tradeoffs involved—while proper labeling significantly improves model accuracy and performance, it's often expensive and time-consuming. The episode concludes by sharing best practices like consensus measurement, label auditing, and active learning techniques that help organizations optimize their data labeling processes for maximum efficiency and accuracy across various use cases from image recognition to sentiment analysis. Find more information at <a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Ian Smalley

DESCRIPTION

This episode of Techsplainers explores data labeling, the critical preprocessing stage where raw data is assigned contextual tags to make it intelligible for machine learning models. We examine how this process combines software tools with human-in-the-loop participation to create the foundation for AI applications like computer vision and natural language processing. The podcast compares five distinct approaches to data labeling: internal labeling (using in-house experts), synthetic labeling (generating new data from existing datasets), programmatic labeling (automating the process through scripts), outsourcing (leveraging external specialists), and crowdsourcing (distributing micro-tasks across many contributors). We also discuss the tradeoffs involved—while proper labeling significantly improves model accuracy and performance, it's often expensive and time-consuming. The episode concludes by sharing best practices like consensus measurement, label auditing, and active learning techniques that help organizations optimize their data labeling processes for maximum efficiency and accuracy across various use cases from image recognition to sentiment analysis.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley