Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Amazon_Web_Services
|
| gptkbp:automated |
data labeling tasks
|
| gptkbp:can_be_used_for |
image classification
object detection video labeling text classification |
| gptkbp:can_handle |
large datasets
|
| gptkbp:enables |
custom labeling workflows
|
| gptkbp:has |
a user-friendly interface
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sage Maker Ground Truth
|
| gptkbp:integrates_with |
gptkb:Sage
|
| gptkbp:is_accessible_by |
gptkb:AWS_SDKs
gptkb:AWS_Management_Console gptkb:AWS_CLI |
| gptkbp:is_available_in |
multiple AWS regions
|
| gptkbp:is_compatible_with |
gptkb:Tensor_Flow
gptkb:MXNet gptkb:scikit-learn gptkb:Py_Torch |
| gptkbp:is_designed_for |
gptkb:developers
business analysts data scientists |
| gptkbp:is_effective_against |
for data labeling projects
|
| gptkbp:is_integrated_with |
third-party tools
|
| gptkbp:is_part_of |
AWS ecosystem
AWS AI services |
| gptkbp:is_scalable |
to meet project needs
|
| gptkbp:is_supported_by |
AWS documentation
AWS support services |
| gptkbp:is_updated_by |
with new features
|
| gptkbp:is_used_for |
training machine learning models
enhancing data quality improving model accuracy reducing labeling costs |
| gptkbp:is_used_in |
automotive applications
healthcare applications robotics applications retail applications agriculture applications education applications manufacturing applications finance applications |
| gptkbp:offers |
data privacy features
human labeling workflows pre-built labeling workflows |
| gptkbp:provides |
detailed reporting
annotation tools data labeling services quality control features |
| gptkbp:security |
with AWS security features
|
| gptkbp:supports |
gptkb:machine_learning
multiple data formats multi-language labeling |
| gptkbp:utilizes |
active learning
|
| gptkbp:bfsParent |
gptkb:AWS_Sage_Maker
gptkb:Sage |
| gptkbp:bfsLayer |
5
|