Statements (71)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Sage
|
| gptkbp:allows |
custom metrics
|
| gptkbp:analyzes |
training metrics
Tensor Flow models Py Torch models |
| gptkbp:assets |
research purposes
|
| gptkbp:can_be_configured_for |
specific use cases
|
| gptkbp:can_be_used_for |
hyperparameter tuning
|
| gptkbp:can_be_used_in |
production environments
|
| gptkbp:can_be_used_to |
validate models
track experiments |
| gptkbp:can_create |
debugging reports
|
| gptkbp:can_provide |
model deployment
error detection insights into training process |
| gptkbp:captures |
training data
|
| gptkbp:developed_by |
gptkb:Amazon_Web_Services
|
| gptkbp:enables |
data visualization
|
| gptkbp:enhances |
collaboration among teams
model interpretability |
| gptkbp:facilitates |
collaborative debugging
faster iterations |
| gptkbp:helps |
identify training issues
|
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon Sage Maker Debugger
|
| gptkbp:integrates_with |
gptkb:AWS_Lambda
gptkb:Sage |
| gptkbp:is_accessible_by |
gptkb:API
gptkb:AWS_CLI |
| gptkbp:is_available_for |
various programming languages
|
| gptkbp:is_available_in |
gptkb:AWS_Management_Console
|
| gptkbp:is_beneficial_for |
AI researchers
|
| gptkbp:is_compatible_with |
gptkb:Jupyter_notebooks
various frameworks data lakes |
| gptkbp:is_designed_for |
enterprise applications
|
| gptkbp:is_documented_in |
AWS documentation
|
| gptkbp:is_effective_against |
debugging complex models
|
| gptkbp:is_integrated_with |
gptkb:AWS_Cloud_Watch
gptkb:AWS_Sage_Maker_Studio |
| gptkbp:is_optimized_for |
cloud environments
|
| gptkbp:is_part_of |
AWS ecosystem
cloud-based solutions machine learning lifecycle data science toolkit AI development process AI/ ML tools |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_used_by |
data scientists
|
| gptkbp:is_used_for |
data-driven decision making
model training |
| gptkbp:is_used_in |
data analysis
|
| gptkbp:is_used_to |
improve model accuracy
|
| gptkbp:is_utilized_for |
gptkb:developers
training efficiency streamline workflows |
| gptkbp:offers |
automated debugging
|
| gptkbp:provides |
real-time monitoring
alerts for anomalies visual debugging tools metrics tracking |
| gptkbp:provides_support_for |
data preprocessing
model optimization |
| gptkbp:support |
real-time analytics
multi-user environments data governance. |
| gptkbp:supports |
model evaluation
machine learning models debugging workflows |
| gptkbp:track |
model performance
|
| gptkbp:bfsParent |
gptkb:AWS
|
| gptkbp:bfsLayer |
4
|