AWS SageMaker
PAIDFully managed machine learning service on AWS
► Build production ML systems with managed infrastructure
Product Details
■ INTELLIGENCE BRIEFING — Weekly tool drops. No spam.
PROS & CONS
STRENGTHS
- End-to-end ML platform covering data prep through model deployment
- Deep integration with AWS services for data, compute, and storage
- JumpStart provides pre-trained models and solution templates
WEAKNESSES
- −Complex pricing model can make cost estimation difficult
- −Vendor lock-in with AWS-specific APIs and infrastructure
KEY FEATURES
Studio
IDE for building, training, and deploying ML models
JumpStart
Pre-trained models and solutions for common ML tasks
Pipelines
CI/CD for ML with automated training and deployment
Inference
Real-time and batch model hosting with auto-scaling
WHO IS AWS SageMaker BEST FOR?
Enterprise ML teams on AWS
Build production ML systems with managed infrastructure
Data science teams
Collaborate on model development with shared notebooks and experiments
INTEGRATIONS
TECHNICAL DETAILS
FIELD REPORTS (0)
No field reports yet. Be the first to review AWS SageMaker.
DOSSIER
- COMPANY
- AWS SageMaker
- HQ
- Seattle, United States
- FOUNDED
- 2017
FILED UNDER
PRICING MODEL
BEST FOR
FINAL ASSESSMENT
RELATED FILES
Similar tools in the same category
H2O.ai
FREEMIUMOpen-source and enterprise AI and ML platform
DataRobot
PAIDEnterprise AI platform for automated machine learning
Kaggle
FREEData science competition platform and learning community
Jupyter
FREEInteractive computing notebooks for data science and ML