AWS Machine Learning services hub — Amazon SageMaker AI for building, training, and deploying ML models.
Overview
AWS provides fully managed ML infrastructure through Amazon SageMaker AI — a comprehensive platform for the entire ML lifecycle.
Amazon SageMaker Platform
The next-generation Amazon SageMaker is now the center for all data, analytics, and AI:
| Component | Purpose |
|---|---|
| SageMaker AI | Build, train, deploy ML and foundation models |
| SageMaker Unified Studio | Single IDE for analytics and AI development |
| SageMaker JumpStart | Pre-built models and solutions hub |
| SageMaker Catalog | Data and AI governance |
| Lakehouse Architecture | Unified data across S3 and Redshift |
Notes
Amazon SageMaker
- Overview — What, why, benefits, pricing
- Data Tools — Data processing, notebooks, lakehouse
- Models — JumpStart, model customization, inference
- Governance — Catalog, lineage, quality monitoring
- Features — HyperPod, MLflow, Studio, MLOps
Access
🔴 AWS account required for all SageMaker services 🟡 Free tier available for Unified Studio and some features
Quick Comparison: SageMaker AI vs Bedrock
| Aspect | SageMaker AI | Bedrock |
|---|---|---|
| Focus | Custom ML model development | Pre-built foundation models |
| Skill level | Data scientists, ML engineers | Developers, all skill levels |
| Model training | Full control, custom training | Fine-tuning only |
| Infrastructure | Managed compute (HyperPod) | Fully serverless |
| Best for | Custom models, full lifecycle | GenAI apps, quick deployment |