Developing ML solutions
Developing ML Solutions with Amazon SageMaker AI
Amazon SageMaker AI is a fully managed ML service for the whole workflow in one interface: collect and prepare data, build and train models, and deploy and monitor them. SageMaker Studio is the recommended web-based UI for all of it.
SageMaker features by lifecycle stage
| Stage | Feature |
|---|---|
| Label data | SageMaker Ground Truth: human labeling, including RLHF |
| Prepare data | SageMaker Data Wrangler: low-code import, cleaning, transformation, and analysis |
| Store features | SageMaker Feature Store: shared, reusable features for training and inference |
| Detect bias and explain | SageMaker Clarify |
| Build without code | SageMaker Canvas: no-code visual ML for business analysts |
| Start from pre-trained models | SageMaker JumpStart |
| Automate model building | SageMaker Autopilot: AutoML |
| Train and tune | Managed training jobs and automatic model tuning (hyperparameter optimization) |
| Track experiments | SageMaker Experiments |
| Version and approve models | SageMaker Model Registry |
| Orchestrate pipelines | SageMaker Pipelines: CI/CD for ML |
| Monitor in production | SageMaker Model Monitor |
The feature list comes from the SageMaker diagrams, which weren't captured as text in the full notes.