Statements (59)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Amazon SageMaker
|
| gptkbp:allows |
data scientists to build and train machine learning models
|
| gptkbp:basedOn |
usage
|
| gptkbp:collaborates_with |
other users
|
| gptkbp:engineConfiguration |
custom_Docker_containers
|
| https://www.w3.org/2000/01/rdf-schema#label |
SageMaker Notebooks
|
| gptkbp:integration |
gptkb:Amazon_S3
|
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
JupyterLab_interface Jupyter_Notebook_interface |
| gptkbp:is_available_in |
multiple_AWS_regions
|
| gptkbp:is_designed_to |
machine learning workflows
|
| gptkbp:is_integrated_with |
gptkb:AWS_Glue
gptkb:AWS_Lambda Amazon Redshift |
| gptkbp:is_part_of |
AWS_ecosystem
|
| gptkbp:is_used_in |
data visualization
hyperparameter tuning model evaluation data exploration model training transform data share insights clean data create batch transform jobs create training jobs deploy models document experiments monitor models prepare data run experiments visualize model performance create_dashboards |
| gptkbp:launched |
gptkb:AWS_CLI
AWS_SDKs |
| gptkbp:maintainedBy |
gptkb:Amazon_Web_Services
|
| gptkbp:offers |
pre-built machine learning algorithms
|
| gptkbp:performance |
cloud computing
large datasets |
| gptkbp:provides |
automatic scaling
collaborative features interactive data analysis built-in security features Jupyter Notebook environment notebook lifecycle configuration |
| gptkbp:suitableFor |
real-time inference
batch inference |
| gptkbp:supports |
gptkb:PyTorch
gptkb:Julia Python TensorFlow R multiple instance types version control MXNet data import from various sources IAM_roles GPU_instances CPU_instances |