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Data Labeling Overview for Machine Learning and Data Science

Learn the core aspects of data labeling — data, process, people, and technology and how to build a successful data labeling system.

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While data is critical for Al, raw data doesn't come with enough context to train a machine learning model. This knowledge leads teams to adopt a data-centric approach -focusing on the quality of data to produce better machine learning outcomes.

At the core of this data-centric approach is data labeling - a layer of metadata that connects raw data to the predictions your machine learning model is learning to make.

What's Covered

  • The core aspects of data labeling, including data, process, people, and technology.
  • How to organize the key components of data labeling to build a successful, efficient, and repeatable data labeling system for your organization.