|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:allows
|
Data preparation
|
|
gptkbp:can_be_used_with
|
Database connections
CSV data
Excel data
|
|
gptkbp:can_create
|
Reports
|
|
gptkbp:developed_by
|
gptkb:server
|
|
gptkbp:enables
|
Model building
|
|
gptkbp:features
|
Automated machine learning
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Amazon Sage Maker Canvas
|
|
gptkbp:integrates_with
|
gptkb:temple
Third-party tools
|
|
gptkbp:is_accessible_by
|
Web interface
|
|
gptkbp:is_available_in
|
Multiple regions
|
|
gptkbp:is_compared_to
|
Other ML platforms
|
|
gptkbp:is_compatible_with
|
gptkb:HIPAA
gptkb:GDPR
|
|
gptkbp:is_designed_for
|
Non-technical users
|
|
gptkbp:is_effective_against
|
gptkb:theorem
|
|
gptkbp:is_enhanced_by
|
Statistical methods
Machine learning algorithms
Data science techniques
|
|
gptkbp:is_evaluated_by
|
User reviews
Industry analysts
|
|
gptkbp:is_optimized_for
|
gptkb:Cloud_Computing_Service
|
|
gptkbp:is_part_of
|
AWS ecosystem
AI and ML solutions
Amazon Sage Maker suite
|
|
gptkbp:is_promoted_by
|
AWS marketing
|
|
gptkbp:is_scalable
|
Large datasets
|
|
gptkbp:is_supported_by
|
AWS documentation
AWS support
|
|
gptkbp:is_used_by
|
gptkb:physicist
Enterprises
Startups
|
|
gptkbp:is_used_for
|
Business intelligence
|
|
gptkbp:is_utilized_in
|
Data analysis
Forecasting
Predictive modeling
|
|
gptkbp:offers
|
Predictive analytics
Collaboration features
|
|
gptkbp:primary_user
|
Create machine learning models
|
|
gptkbp:provides
|
Model evaluation tools
Model deployment options
No-code machine learning capabilities
|
|
gptkbp:provides_access_to
|
Pre-built algorithms
|
|
gptkbp:release_date
|
gptkb:2021
|
|
gptkbp:requires
|
AWS account
|
|
gptkbp:security_features
|
Data encryption
|
|
gptkbp:supports
|
Data visualization
Multiple data sources
|
|
gptkbp:targets
|
Business analysts
Data scientists
|
|
gptkbp:user_experience
|
gptkb:theorem
|
|
gptkbp:bfsParent
|
gptkb:AWS
|
|
gptkbp:bfsLayer
|
3
|