Statements (60)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:railway_line
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:temple
|
| gptkbp:analyzes |
input data
predictions |
| gptkbp:can_be_used_with |
batch transform jobs
endpoint deployments |
| gptkbp:can_create |
gptkb:report
dashboards alerts |
| gptkbp:can_provide |
insights
performance metrics regulatory compliance |
| gptkbp:enables |
automated monitoring
|
| gptkbp:facilitates |
model governance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sage Maker Model Monitor
|
| gptkbp:integrates_with |
gptkb:aircraft
gptkb:temple gptkb:Amazon_Redshift gptkb:Amazon_S3 gptkb:seal |
| gptkbp:is_a_tool_for |
report generation
|
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
AWSCLI AWSSD Ks |
| gptkbp:is_available_in |
multiple AWS regions
|
| gptkbp:is_compatible_with |
various data sources
|
| gptkbp:is_designed_for |
production models
|
| gptkbp:is_essential_for |
AI model deployment
|
| gptkbp:is_optimized_for |
cloud environments
|
| gptkbp:is_part_of |
AWS ecosystem
machine learning lifecycle ML Ops |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
data scientists
machine learning engineers |
| gptkbp:is_used_for |
AI applications
reduce operational risks improve model accuracy |
| gptkbp:managed_by |
gptkb:AWS
|
| gptkbp:monitors |
multiple models
|
| gptkbp:notifications |
anomalies
|
| gptkbp:provides |
data visualization
real-time monitoring visualization tools model performance monitoring |
| gptkbp:recognizes |
concept drift
data drift |
| gptkbp:requires |
Sage Maker training jobs
|
| gptkbp:setting |
notifications
alerting |
| gptkbp:supports |
model evaluation
custom metrics model retraining data quality monitoring model comparison A/ B testing model bias detection |
| gptkbp:user_experience |
non-technical users
|
| gptkbp:uses |
Cloud Watch for monitoring
|