Statements (134)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Integrated_Development_Environment
|
| gptkbp:allows |
Data preparation
Model training Integration with Git Custom algorithm integration |
| gptkbp:developed_by |
gptkb:Amazon_Web_Services
|
| gptkbp:enables |
Data scientists
Hyperparameter optimization Data exploration Model training Experiment tracking Scalable training |
| gptkbp:facilitates |
Model evaluation
Model deployment |
| gptkbp:features |
Collaboration tools
Automated model tuning |
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon Sage Maker Studio
|
| gptkbp:includes |
Code editor
Built-in algorithms Experiment management Pre-built environments |
| gptkbp:integrates_with |
gptkb:Amazon_RDS
gptkb:Amazon_Cloud_Watch gptkb:Amazon_S3 gptkb:AWS_Lambda gptkb:Amazon_EFS gptkb:Amazon_EC2 AWS services |
| gptkbp:is_accessible_by |
Web interface
|
| gptkbp:is_available_in |
Multiple regions
|
| gptkbp:is_compatible_with |
gptkb:Tensor_Flow
gptkb:Apache_Spark gptkb:Py_Torch gptkb:Scikit-learn |
| gptkbp:is_designed_for |
Machine learning workflows
Data scientists and developers |
| gptkbp:is_part_of |
gptkb:Sage
AWS ecosystem |
| gptkbp:is_used_for |
Data analysis
Statistical analysis Data-driven decision making Predictive analytics Feature engineering Algorithm development AI model development Deep learning projects Building machine learning models |
| gptkbp:offers |
gptkb:Community_support
Scalability Resource management Data visualization tools Collaboration features Data transformation tools Cloud storage integration User management features Built-in algorithms Cost management tools Custom container support Experiment management Experiment reproducibility Model tuning Training job management Data storage options Resource provisioning tools Model serving capabilities Auto ML capabilities Pre-built environments Model comparison tools Model deployment strategies |
| gptkbp:provides |
gptkb:Documentation
gptkb:user_interface User authentication Resource management User-friendly interface API access Security features Monitoring tools Real-time predictions User management Collaboration features Cost management tools Data labeling Model evaluation tools Data exploration tools Machine learning tools Integration with Git Training metrics Integration with data lakes Integration with BI tools Model performance tracking Integration with Apache Spark Integration with Keras Integration with ONNX Integration with Py Torch Integration with Scikit-learn Integration with Tensor Flow |
| gptkbp:supports |
gptkb:Jupyter_notebooks
Data analysis Version control Data security Real-time collaboration Data governance Data preprocessing Multiple programming languages Data augmentation Collaborative projects Deployment pipelines Data transformation Hyperparameter optimization Containerized applications Data cleaning Data pipelines Data exploration Data ingestion Multi-user environments Real-time inference Experiment tracking Model serving Multi-tenant architecture Custom algorithms Data labeling Data science projects Batch transform jobs Data science workflows Model explainability Model lifecycle management Model monitoring Model retraining Model versioning Data science education Model performance tracking Model accuracy improvement |
| gptkbp:bfsParent |
gptkb:AWS
|
| gptkbp:bfsLayer |
4
|