|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
3
|
|
gptkbp:bfsParent
|
gptkb:temple
|
|
gptkbp:developed_by
|
gptkb:server
|
|
gptkbp:enables
|
deployment of machine learning models
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Sage Maker Neo
|
|
gptkbp:improves
|
gptkb:resource_utilization
|
|
gptkbp:integrates_with
|
gptkb:Sage_Maker
|
|
gptkbp:is_available_in
|
multiple regions
|
|
gptkbp:is_available_on
|
gptkb:AWS_Management_Console
AWSCLI
AWSSD Ks
|
|
gptkbp:is_compatible_with
|
gptkb:garden
gptkb:aircraft
gptkb:fortification
gptkb:AWS_Io_T
Docker containers
|
|
gptkbp:is_designed_for
|
gptkb:software
|
|
gptkbp:is_documented_in
|
AWS documentation
|
|
gptkbp:is_integrated_with
|
gptkb:Amazon_Cloud_Watch
gptkb:Amazon_S3
gptkb:Amazon_ECR
CI/ CD pipelines
|
|
gptkbp:is_optimized_for
|
cloud environments
edge computing
machine learning models
deep learning models
inference performance
|
|
gptkbp:is_part_of
|
gptkb:AWS_Sage_Maker
AWS ecosystem
AWSAI services
AI/ ML solutions
|
|
gptkbp:is_scalable
|
large datasets
|
|
gptkbp:is_supported_by
|
AWS support
|
|
gptkbp:is_used_by
|
data scientists
machine learning engineers
|
|
gptkbp:is_used_for
|
model evaluation
edge devices
model training
real-time inference
batch inference
model lifecycle management
model deployment strategies
|
|
gptkbp:is_used_in
|
production environments
|
|
gptkbp:offers
|
customization options
multi-platform support
|
|
gptkbp:provides
|
API access
scalability
user-friendly interface
model monitoring
model deployment capabilities
automatic model compilation
|
|
gptkbp:reduces
|
latency
costs
|
|
gptkbp:security_features
|
data handling
|
|
gptkbp:supports
|
gptkb:Graphics_Processing_Unit
gptkb:CEO
gptkb:Py_Torch
model versioning
model optimization
MX Net
|
|
gptkbp:works_with
|
multiple frameworks
|