Amazon Sage Maker Studio Experiments
GPTKB entity
Statements (63)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Sage
|
| gptkbp:allows |
custom metrics tracking
model comparison parameter optimization |
| gptkbp:can_be_used_for |
feature engineering
A/ B testing |
| gptkbp:can_be_used_with |
various ML frameworks
|
| gptkbp:enables |
automated model evaluation
reproducibility data versioning experiment sharing |
| gptkbp:facilitates |
collaboration among data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon Sage Maker Studio Experiments
|
| gptkbp:integrates_with |
gptkb:Sage
|
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
gptkb:AWS_CLI gptkb:AWS_IAM_users |
| gptkbp:is_available_in |
multiple AWS regions
|
| gptkbp:is_compatible_with |
gptkb:Tensor_Flow
gptkb:Keras gptkb:MXNet gptkb:Py_Torch gptkb:Scikit-learn Docker containers Python SDK |
| gptkbp:is_designed_for |
machine learning experiments
|
| gptkbp:is_integrated_with |
gptkb:Amazon_Cloud_Watch
gptkb:Amazon_S3 CI/ CD pipelines |
| gptkbp:is_optimized_for |
gptkb:cloud_computing
AWS infrastructure |
| gptkbp:is_part_of |
AWS ecosystem
AWS AI services machine learning workflow data science lifecycle AWS machine learning services |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
data scientists
|
| gptkbp:is_used_for |
data analysis
model deployment model training |
| gptkbp:offers |
API access
real-time monitoring visualization tools |
| gptkbp:provides |
experiment tracking
experiment results experiment insights experiment organization experiment summaries experiment history experiment metadata experiment visualization |
| gptkbp:supports |
gptkb:Jupyter_notebooks
team collaboration data preprocessing hyperparameter tuning model selection model evaluation batch processing data labeling multiple experiment runs |
| gptkbp:bfsParent |
gptkb:Amazon_Web_Services_AI
|
| gptkbp:bfsLayer |
5
|