AI Platform Data Labeling Service lets you work with human labelers to generate highly accurate labels for a collection of data that you can use in machine learning models.
Labeling your training data is the first step in the machine learning development cycle. To train a machine learning model, provide representative data samples that you want to classify or analyze, along with the machine learning algorithm to handle each sample. For example, to train a model that can identify flowers in images, you must label objects like sunflowers, roses, and tulips in the image dataset. To train a model that can identify the names of diseases in medical documents, you must highlight disease-related words in the document dataset.
To start data labeling in AI Platform Data Labeling Service, create three resources for the human labelers:
- A dataset containing the representative data samples to label
- A label set listing all possible labels in the dataset
- A set of instructions guiding human labelers through labeling tasks
Once you've created these resources, you submit them as part of a labeling request. The human labelers start annotating the items in the dataset according to your instructions. After human labelers finish the labeling, you can export well labeled datasets and use the datasets in the machine learning development.
Learn about Data Labeling Service pricing.