Amazon SageMaker Experiments
GPTKB entity
Statements (69)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Amazon SageMaker
|
| gptkbp:allows |
comparison of model performance
|
| gptkbp:canBe |
experiment artifacts
model parameters |
| gptkbp:enables |
parameter sweeps
reproducibility of machine learning experiments |
| gptkbp:facilitates |
collaboration among data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon SageMaker Experiments
|
| gptkbp:integratesWith |
Amazon SageMaker
|
| gptkbp:integration |
CI/CD pipelines
|
| gptkbp:isAccessibleBy |
gptkb:AWS_Management_Console
|
| gptkbp:isAvailableIn |
startups and enterprises
various programming languages multiple_AWS_regions |
| gptkbp:isCompatibleWith |
gptkb:AWS_Lambda
Docker containers |
| gptkbp:isCounteredBy |
AWS_SDKs
|
| gptkbp:isDesignedFor |
data scientists
iterative development |
| gptkbp:isIntegratedWith |
gptkb:Amazon_CloudWatch
gptkb:AWS_Glue gptkb:Amazon_S3 Amazon Redshift |
| gptkbp:isOptimizedFor |
high-performance computing
large-scale machine learning cloud-based environments |
| gptkbp:isPartOf |
machine learning workflow
data science toolkit AWS_ecosystem AWS_AI_services AWS_data_analytics_services AWS_machine_learning_services |
| gptkbp:isSuitableFor |
enterprise applications
|
| gptkbp:isUsedFor |
Jupyter notebooks
model evaluation various ML frameworks monitor training jobs track model versions track resource utilization analyze experiment outcomes automate experiment workflows conduct A/B testing evaluate model fairness log experiment parameters manage data versions manage experiment configurations manage experiment dependencies manage experiment lifecycle manage experiment outcomes store experiment results track data drift track feature importance track model accuracy track training duration visualize training metrics |
| gptkbp:mayHave |
experiment reports
|
| gptkbp:offers |
visualization of experiment results
|
| gptkbp:provides |
API for programmatic access
experiment tracking experiment lineage tracking experiment tagging metadata management for experiments |
| gptkbp:supports |
data preprocessing
hyperparameter tuning custom metrics multi-user access real-time inference multiple experiment runs automated experiment management |