Statements (70)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Integrated_Development_Environment
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:temple
|
| gptkbp:allows |
Collaboration among data scientists
Custom model development |
| gptkbp:developed_by |
gptkb:server
|
| gptkbp:enables |
Model training
Model monitoring |
| gptkbp:facilitates |
Data preparation
|
| gptkbp:features |
Code editor
|
| https://www.w3.org/2000/01/rdf-schema#label |
Sage Maker Studio
|
| gptkbp:includes |
Experiment management
|
| gptkbp:integrates_with |
AWS services
|
| gptkbp:is_accessible_by |
Web interface
|
| gptkbp:is_compatible_with |
gptkb:Git
|
| gptkbp:is_designed_for |
Data scientists
Machine learning workflows |
| gptkbp:is_part_of |
gptkb:temple
AWS ecosystem |
| gptkbp:is_scalable |
gptkb:battle
|
| gptkbp:is_used_for |
Data analysis
Business intelligence Predictive analytics Data preprocessing Data transformation Feature engineering Hyperparameter tuning Model evaluation Data exploration Data ingestion Model serving Data science projects |
| gptkbp:is_used_in |
gptkb:Cloud_Computing_Service
|
| gptkbp:offers |
Resource optimization
Interactive dashboards Interactive development Experiment tracking Built-in algorithms Deployment options Customizable environments Integrated debugging tools Cloud-based resources Pre-built environments |
| gptkbp:provides |
Collaboration tools
Performance monitoring Real-time collaboration Resource management API access Visualizations Security features User management Collaboration features Access to datasets Machine Learning tools Integration with ID Es |
| gptkbp:supports |
gptkb:Auto_ML
gptkb:Jupyter_notebooks Version control Batch processing Data governance Multiple programming languages Third-party integrations Containerized applications Data pipelines Multi-user access Real-time inference Model deployment Data labeling Data visualization libraries Model retraining |