Amazon SageMaker Data Labeling
Amazon SageMaker Data Labeling is a service that makes it easy to label your datasets for machine learning. You can request human labelers from a pre-qualified workforce and manage them at scale.
machine-learning
data-labeling
training-data
Amazon SageMaker Data Labeling
Amazon SageMaker Data Labeling is a service that makes it easy to label your datasets for machine learning. You can request human labelers from a pre-qualified workforce and manage them at scale.
What is Amazon SageMaker Data Labeling?
Amazon SageMaker Data Labeling is a service that provides access to human labelers so that you can easily and quickly label large datasets for machine learning. Some key features include:
- Get access to a workforce of pre-qualified human labelers who can label images, videos, text, and more for your machine learning datasets
- Use built-in templates for common labeling tasks like image classification, object detection, text classification etc. or build fully customized workflows
- Scale to thousands of human labelers working in parallel to label massive datasets with millions of records
- Monitoring, tracking and management of human labelers to ensure high quality labels
- Integration with other AWS services like Amazon SageMaker Ground Truth for automated data verification
Amazon SageMaker Data Labeling helps save time and effort spent managing large scale human labeling workflows. The service reduces the undifferentiated heavy lifting associated with sourcing, vetting and managing a global workforce of human labelers so you can focus on developing high quality machine learning models faster.
Amazon SageMaker Data Labeling Features
Features
- Automated data labeling with pre-built algorithms
- Access to on-demand workforce for data labeling
- Integration with Amazon SageMaker for training models
- Support for image, text, and video labeling
- Management console to track labeling progress
- API access for custom labeling workflows
Pricing
- Pay-As-You-Go
Pros
Reduces time spent labeling datasets
Scales to large datasets with on-demand workforce
Tight integration with Amazon SageMaker simplifies model building workflow
Supports common data types like images, text and video out of the box
Console provides visibility into labeling progress and costs
Cons
Limited to AWS ecosystem
Data labeling quality dependent on workforce skills
Algorithms may not produce high quality training data
Additional costs for data labeling workforce
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