About Data Labeling
Find out about Data Labeling, what it is and how to use it.
Data labeling is the process of identifying properties (labels) of documents, text, and images (records), and annotating (labeling) them with those properties. The topic of a news article, the sentiment of a tweet, the caption of an image, important words spoken in an audio recording, the genre of a video are all examples of a data label.
Many machine learning techniques require labeled data before they can be used to train machines to complete an autonomous task. Data labeling is thus an integral part of an Artificial Intelligence (AI) or Machine Learning (ML) project.
Data Labeling lets you create and browse datasets, view data records (documents, text, and images), and apply labels to build AI/ML models. Datasets can be exported as line-delimited JSON for use in machine learning model development. Datasets are accessible and interoperable across other Data and AI services to support supervised training. For example, Oracle Cloud Infrastructure Language can be used to create specialized models, but only if labeled data is available for training the model. Data Labeling lets you quickly start labeling raw datasets with a minimal number of configuration steps. Thus, it also provides the data labeling experience for Oracle Cloud Infrastructure AI services.
More information on the supported file types and content types for documents, text, and images is given in Supported File Formats.
Watch a video introduction to the service..