DJames617 AWS Service
Amazon SageMaker AI
SageMaker AI supports the ML lifecycle: data prep, training, tuning, model registry, deployment, and monitoring.
What This Service Solves
SageMaker AI supports the ML lifecycle: data prep, training, tuning, model registry, deployment, and monitoring.
- Use SageMaker for custom ML model development, training jobs, managed endpoints, pipelines, model registry, and ML operations.
When You Should Not Use It
- Avoid custom SageMaker workflows when a managed AI service or Bedrock foundation model solves the requirement with less overhead.
What AWS Manages and What You Manage
AWS manages ML infrastructure services. You manage data, algorithms, containers, training code, endpoint choices, access, evaluation, and monitoring.
Security Implications
Use IAM roles, VPC isolation where needed, KMS, private data paths, Model Registry approvals, and artifact access control.
Availability and Scaling
Deploy endpoints across production variants and monitor health. Batch transform is better for offline scoring.
Endpoint auto scaling, instance choice, and batch sizing affect performance and cost.
Cost Behavior
Training instances, endpoint uptime, storage, processing jobs, and experiments drive cost.
Common Integrations
- S3
- ECR
- CloudWatch
- Step Functions
- CodePipeline
- Glue
How AWS Might Present It
Certification-Specific Depth
AI Practitioner
Know what the service does and when it is the right family.
Machine Learning Engineer Associate
Know configuration choices, integrations, failure modes, security, operations, and cost tradeoffs.
Related Comparisons
Sources and Review Metadata
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