Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:AI_technology
|
| gptkbp:allows |
automatic feature engineering
|
| gptkbp:automated |
model selection process
|
| gptkbp:can_be_used_for |
time series forecasting
anomaly detection |
| gptkbp:can_be_used_in |
real-time applications
batch processing applications |
| gptkbp:can_create |
explanatory reports
multiple model candidates |
| gptkbp:can_handle |
unstructured data
structured data |
| gptkbp:deployment |
machine learning models
|
| gptkbp:developed_by |
gptkb:Amazon_Web_Services
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sage Maker Autopilot
|
| gptkbp:integrates_with |
gptkb:Amazon_RDS
gptkb:Amazon_Redshift gptkb:Amazon_S3 gptkb:AWS_Lambda gptkb:Sage |
| gptkbp:is_accessible_by |
gptkb:AWS_SDKs
gptkb:API gptkb:AWS_Management_Console gptkb:AWS_CLI |
| gptkbp:is_available_in |
multiple AWS regions
|
| gptkbp:is_effective_against |
small to medium businesses
|
| gptkbp:is_optimized_for |
cloud environments
|
| gptkbp:is_part_of |
gptkb:AWS_Machine_Learning_suite
AWS ecosystem AWS AI services |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
enterprises
|
| gptkbp:is_used_for |
predictive analytics
|
| gptkbp:is_user_friendly |
non-experts
|
| gptkbp:offers |
data visualization tools
security features hyperparameter optimization custom model tuning |
| gptkbp:provides |
model explainability features
automated machine learning capabilities model retraining capabilities model performance metrics data import/export features data labeling capabilities Jupyter notebook integration |
| gptkbp:suitable_for |
gptkb:developers
business analysts data scientists |
| gptkbp:supports |
data preprocessing
classification tasks model evaluation compliance standards model training regression tasks multi-model endpoints |
| gptkbp:bfsParent |
gptkb:AWS_Sage_Maker
gptkb:Sage |
| gptkbp:bfsLayer |
5
|