Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Cloud_Computing_Service
|
| gptkbp:allows |
custom pipeline steps
|
| gptkbp:can_be |
events_from_AWS_services
|
| gptkbp:compatibleWith |
various ML frameworks
|
| gptkbp:constructedIn |
serverless architecture
|
| gptkbp:enables |
CI/CD for machine learning
|
| https://www.w3.org/2000/01/rdf-schema#label |
AWS SageMaker Pipelines
|
| gptkbp:integratesWith |
gptkb:AWS_Glue
gptkb:Amazon_S3 gptkb:AWS_Lambda Amazon SageMaker |
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
gptkb:AWS_CLI AWS_SDKs |
| gptkbp:is_available_in |
multiple_AWS_regions
|
| gptkbp:is_designed_to |
data scientists
machine learning engineers reduce_time_to_market_for_ML_models |
| gptkbp:is_integrated_with |
gptkb:Amazon_CloudWatch
|
| gptkbp:is_monitored_by |
gptkb:AWS_CloudTrail
|
| gptkbp:is_part_of |
AWS SageMaker
AWS AI/ML ecosystem |
| gptkbp:is_used_in |
Jupyter notebooks
third-party tools automate data workflows end-to-end machine learning workflows automated retraining collaborative machine learning projects create reproducible experiments manage ML lifecycle streamline model deployment |
| gptkbp:offers |
visualization tools
|
| gptkbp:provides |
security features
logging and monitoring automated machine learning workflows model deployment capabilities resource management features integration with Git repositories parameter store integration template-based pipeline creation version control for models |
| gptkbp:suitableFor |
large-scale machine learning projects
|
| gptkbp:supports |
data preprocessing
hyperparameter tuning parallel processing A/B testing model evaluation data augmentation custom metrics model training data versioning data drift detection model performance tracking multi-model endpoints model explainability batch transform jobs model registry |
| gptkbp:uses |
step functions
|