Statements (55)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Amazon SageMaker
|
| gptkbp:allows |
custom pipeline steps
|
| gptkbp:enables |
continuous delivery
continuous integration model deployment model retraining |
| gptkbp:facilitates |
collaboration among data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon SageMaker Pipelines
|
| gptkbp:integratesWith |
AWS_services
|
| gptkbp:isAccessibleBy |
gptkb:AWS_Management_Console
gptkb:AWS_CLI AWS_SDKs |
| gptkbp:isAvailableIn |
multiple_AWS_regions
|
| gptkbp:isCompatibleWith |
Docker containers
|
| gptkbp:isDocumentedIn |
AWS documentation
|
| gptkbp:isIntegratedWith |
gptkb:Amazon_CloudWatch
gptkb:Amazon_S3 gptkb:AWS_Lambda gptkb:AWS_IAM gptkb:Amazon_ECR Git repositories Jupyter notebooks |
| gptkbp:isOptimizedFor |
performance
scalability |
| gptkbp:isPartOf |
AWS_ecosystem
MLOps |
| gptkbp:isPromotedBy |
AWS_marketing
|
| gptkbp:isSupportedBy |
AWS_support
|
| gptkbp:isUsedBy |
business analysts
data scientists machine learning engineers |
| gptkbp:isUsedFor |
A/B testing
feature engineering real-time inference batch transform jobs |
| gptkbp:offers |
model monitoring
|
| gptkbp:provides |
API for automation
security features visualization tools logging capabilities reproducibility of experiments cost management features automated machine learning workflows |
| gptkbp:supports |
data preprocessing
hyperparameter tuning model evaluation data augmentation model versioning model training data versioning multiple ML frameworks multi-account setups data drift detection model explainability |
| gptkbp:uses |
step functions
|