Red Hat Open Shift Data Science
GPTKB entity
Statements (87)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:bfsLayer |
6
|
| gptkbp:bfsParent |
gptkb:Open_Shift_AI
|
| gptkbp:constructed_in |
gptkb:Open_Shift_Container_Platform
|
| gptkbp:developed_by |
gptkb:Red_Hat
|
| gptkbp:enables |
Data-driven decision making
Rapid prototyping Collaboration among data scientists Experimentation with data |
| gptkbp:facilitates |
Deployment of AI models
|
| gptkbp:features |
gptkb:Jupyter_Notebooks
|
| https://www.w3.org/2000/01/rdf-schema#label |
Red Hat Open Shift Data Science
|
| gptkbp:includes |
Collaboration features
Model training capabilities |
| gptkbp:integrates_with |
gptkb:chess_match
|
| gptkbp:is_available_for |
On-premises deployment
|
| gptkbp:is_available_on |
Cloud platforms
|
| gptkbp:is_compatible_with |
Third-party tools
Data science frameworks Data analytics platforms Various data storage solutions |
| gptkbp:is_designed_for |
Enterprise use
Collaborative data science projects |
| gptkbp:is_designed_to |
Enhance productivity
Streamline data workflows |
| gptkbp:is_integrated_with |
Business applications
Data lakes Data science libraries |
| gptkbp:is_optimized_for |
gptkb:benchmark
|
| gptkbp:is_part_of |
Cloud-native applications
Digital transformation initiatives AI and ML strategy Red Hat Open Shift ecosystem Data Ops practices |
| gptkbp:is_scalable |
Large datasets
|
| gptkbp:is_used_by |
Data scientists
|
| gptkbp:is_used_for |
Data preparation
Data model management |
| gptkbp:is_used_in |
Predictive analytics
Data exploration |
| gptkbp:is_used_to |
Build machine learning models
|
| gptkbp:is_utilized_in |
Research institutions
Business intelligence Startups and enterprises alike Organizations for data analysis |
| gptkbp:offers |
Collaboration across teams
User management features Flexible deployment options Data integration capabilities Real-time collaboration features Customizable environments Support for data pipelines Support for various frameworks Integration with CI/ CD pipelines Automated machine learning features Data Science workflows Scalability for data processing |
| gptkbp:provides |
User-friendly interface
Security features Monitoring tools Training resources Documentation and support Machine Learning tools Access to pre-built models Version control for models Collaboration tools for teams Access to analytics tools Access to cloud resources Access to cloud-native tools Access to GPU resources |
| gptkbp:provides_access_to |
Data sources
|
| gptkbp:supports |
gptkb:fortification
Real-time data processing Batch processing Data governance Data quality management Model evaluation Multi-cloud environments Data visualization tools Experiment tracking Data analysis and reporting Data sharing among teams Data security compliance Python and R programming languages Data science best practices Model deployment automation |
| gptkbp:utilizes |
gptkb:lake
|