|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
4
|
|
gptkbp:bfsParent
|
gptkb:Sage_Maker_Neo
|
|
gptkbp:community_support
|
AWS Forums
|
|
gptkbp:competes_with
|
gptkb:IBM_Watson_Studio
gptkb:Google_AI_Platform
gptkb:Microsoft_Azure_Machine_Learning
|
|
gptkbp:developed_by
|
gptkb:server
|
|
gptkbp:has_documentation
|
AWS Documentation
|
|
gptkbp:has_feature
|
Scalability
Collaboration tools
Cost management
Security features
Model evaluation
Data preparation
Experiment management
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Sage Maker
|
|
gptkbp:integrates_with
|
gptkb:aircraft
gptkb:Amazon_S3
gptkb:seal
Amazon E C2
|
|
gptkbp:is_available_in
|
Multiple AWS regions
|
|
gptkbp:is_part_of
|
gptkb:AWS_Cloud
AWS ecosystem
|
|
gptkbp:is_used_by
|
gptkb:software
Data scientists
Machine learning engineers
|
|
gptkbp:offers
|
Hyperparameter tuning
Real-time inference
Built-in algorithms
Custom algorithms
Automatic model tuning
Model monitoring
Batch transform
|
|
gptkbp:provides
|
gptkb:Sage_Maker_Autopilot
gptkb:Sage_Maker_Debugger
gptkb:Sage_Maker_Ground_Truth
gptkb:Sage_Maker_Neo
gptkb:Sage_Maker_Studio
Model training
Model deployment
Data labeling
Sage Maker Pipelines
Sage Maker Model Registry
|
|
gptkbp:release_date
|
November 2017
|
|
gptkbp:revenue
|
Pay-as-you-go
Reserved instances
|
|
gptkbp:supports
|
gptkb:Java
gptkb:R
gptkb:Library
gptkb:Skrull
gptkb:Jupyter_notebooks
|
|
gptkbp:tutorials
|
AWS Tutorials
|
|
gptkbp:uses
|
Docker containers
|