Amazon SageMaker

Rebranding Note

‼️ NOTE: in 2025, AWS rebranded SageMaker as "SageMaker AI”
- If you're looking for it in the AWS Console, look for it under that new name
- Just plain "SageMaker" takes you instead to "SageMaker Unified Studio" or the "SageMaker Platform," which is a wrapper around SageMaker and Amazon DataZone
- The exam only covers SageMaker AI at this time. These notes only use “SageMaker”, and it should be understood as a synonym to “SageMaker AI”

SageMaker: end-to-end ML service
Generic features
- SageMaker Automatic Model Tuning (AMT): tune hyperparameters
- SageMaker Deployment & Inference: real-time, serverless, asynchronous, batch transform
- SageMaker Studio: unified interface (IDE) for SageMaker
Data Tools
- SageMaker Data Wrangler: explore and prepare datasets, create features
- SageMaker Feature Store: store features metadata in a central place
Models and humans
- SageMaker Clarify: compare models, explain model outputs, detect bias
- SageMaker Ground Truth: RLHF, humans for model grading and data labeling
Governance
- SageMaker Model Cards: ML model documentation
- SageMaker Role Manager: access control
- SageMaker Model Dashboard: view all your models in one place
- SageMaker Model Monitor: monitoring and alerts for your model
- SageMaker Model Registry: centralized repository to manage ML model versions
- SageMaker Pipelines: CICD for Machine Learning
Consoles
- SageMaker JumpStart: ML model hub & pre-built ML solutions
- SageMaker Canvas: no-code interface for SageMaker
- MLFlow on SageMaker: use MLFlow tracking servers on AWS