Amazon SageMaker is AWS's fully managed machine learning platform that provides tools for building, training, deploying, and managing ML models at scale. Since its launch, SageMaker has evolved into a comprehensive AI/ML development environment spanning from data preparation to production inference, with recent additions focused on foundation model training and unified data analytics. ¹⁾

Launched in March 2025, SageMaker Unified Studio is a collaborative workspace built on top of Amazon DataZone that unifies data and AI asset management across an organization:

• Asset catalog — Publish and discover tables, models, agents, and reports in a central catalog with metadata enrichment
• Serverless notebooks — Launch notebooks in seconds with built-in AI agents for code generation from natural language
• One-click onboarding — Simplified setup for new team members
• Multi-engine support — SQL queries, Python code, and Spark jobs in a single workspace
• Data connectivity — Automatic connections to S3, Redshift, and third-party databases

Adopted by companies including Bayer, NatWest, and Carrier. Over 200 improvements were delivered in 2025. ²⁾

HyperPod provisions resilient clusters for training large-scale AI models including LLMs, diffusion models, and foundation models:

• Automatic fault recovery — Detects and replaces faulty nodes without manual intervention
• Observability — Unified Grafana dashboards for GPU utilization, NVLink bandwidth, CPU pressure, FSx for Lustre usage, and Kubernetes pod lifecycle ³⁾
• Restricted Instance Groups (RIG) — Dedicated compute partitions for foundation model training with Nova Forge
• Quota validation — Automatic pre-creation checks for instance limits, EBS volumes, and VPC quotas ⁴⁾
• EKS integration — Kubernetes-based orchestration of training clusters

• SageMaker Training — Managed training jobs with distributed training, spot instances, and automatic model tuning
• SageMaker Endpoints — Real-time and batch inference endpoints with auto-scaling
• SageMaker Pipelines — CI/CD for ML workflows
• SageMaker Feature Store — Centralized feature repository for training and inference
• SageMaker Ground Truth — Data labeling service with human and automated workflows
• SageMaker Model Monitor — Continuous monitoring for data drift and model quality

SageMaker integrates deeply with the AWS ecosystem:

• S3 for data storage and model artifacts
• EC2 (including P5, Trn1, Inf2 instances) for compute
• FSx for Lustre for high-performance training data access
• CloudWatch and Managed Grafana for monitoring
• IAM for access control and security
• Lambda for serverless inference triggers
• Step Functions for workflow orchestration

• Unified Data Fabric for AI
• AI Superfactory