The full ML lifecycle on AWS — from notebook to production endpoint.
SageMaker puts the entire machine-learning workflow — data prep, training, tuning, and serving — behind one AWS console and one SDK.
Who it's for: Teams that already run on AWS and want managed ML without standing up Kubernetes.
Zero-config notebooks, debugging, and visual design in a single IDE.
Distributed training with managed spot instances and warm pools.
Real-time and batch endpoints with autoscaling.
MLOps pipelines, model registry, and CI/CD for ML.
SageMaker is the default choice if you already live in AWS. In 2026 it pairs tightly with Bedrock for generative AI, and the free tier is generous enough to prototype.