Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:temple
|
| gptkbp:allows |
automatic feature engineering
|
| gptkbp:can_create |
explanatory reports
multiple model candidates |
| gptkbp:controls |
unstructured data
structured data |
| gptkbp:deployment |
machine learning models
|
| gptkbp:developed_by |
gptkb:server
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sage Maker Autopilot
|
| gptkbp:integrates_with |
gptkb:aircraft
gptkb:temple gptkb:Amazon_RDS gptkb:Amazon_Redshift gptkb:Amazon_S3 |
| gptkbp:intelligence |
model selection process
|
| gptkbp:is_accessible_by |
gptkb:API
gptkb:AWS_Management_Console AWSCLI AWSSD Ks |
| 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 AWSAI services |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
enterprises
|
| gptkbp:is_used_for |
predictive analytics
time series forecasting anomaly detection |
| gptkbp:is_used_in |
real-time applications
batch processing applications |
| 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:software
business analysts data scientists |
| gptkbp:supports |
data preprocessing
classification tasks model evaluation compliance standards model training regression tasks multi-model endpoints |
| gptkbp:user_experience |
non-experts
|