Data Labeling Overview for Machine Learning and Data Science

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.