Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Cloud_Computing_Service
|
| gptkbp:allows |
Data scientists to build and train machine learning models
|
| gptkbp:compatibleWith |
Docker containers
|
| gptkbp:deployedTo |
gptkb:Amazon_EC2_instances
|
| gptkbp:enables |
Collaboration among data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon SageMaker Notebooks
|
| gptkbp:integration |
gptkb:Amazon_S3
|
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
|
| gptkbp:is_available_in |
Free tier usage
Multiple AWS regions |
| gptkbp:is_designed_to |
Data analysis
Machine learning workflows |
| gptkbp:is_integrated_with |
gptkb:AWS_Glue
gptkb:AWS_Lambda |
| gptkbp:is_part_of |
Amazon SageMaker
AWS_ecosystem AWS_AI_services |
| gptkbp:is_used_in |
Data scientists
Feature engineering Model evaluation Data exploration Conduct research Machine learning engineers Model training Experiment tracking Model deployment Batch inference Model monitoring Create visualizations Develop applications Prototyping machine learning models Run experiments Share notebooks Hyperparameter_tuning Visualize_data |
| gptkbp:offers |
Resource management tools
Built-in security features Pre-built machine learning algorithms |
| gptkbp:performance |
Machine learning tasks
|
| gptkbp:provides |
Access to datasets
Interactive data analysis Customizable environments Access to machine learning libraries Jupyter Notebook environment Scalable compute resources Access_to_AWS_services |
| gptkbp:suitableFor |
Educational purposes
|
| gptkbp:supports |
Python
R Collaboration tools Version control Data preprocessing Multiple programming languages Machine learning frameworks Data import/export Real-time inference Interactive computing |