01 What is it?
AWS SageMaker is the managed platform for the full ML lifecycle, from data labelling and training to deployment, monitoring and governance. SageMaker now includes managed endpoints for foundation models, native vector stores and built-in model evaluation, with deep AWS integration.
02 Why implement it?
- Managed end-to-end ML lifecycle on AWS
- Native integration with IAM, KMS, PrivateLink and CloudTrail
- Foundation-model deployment via JumpStart and Bedrock interop
- Built-in model monitoring, drift detection and Clarify
- Strong compliance posture (HIPAA, SOC 2, FedRAMP)
03 How I help
I design SageMaker architectures aligned to your security boundary: VPC isolation, KMS-encrypted artefacts, fine-grained IAM, model approval workflow, and integration with your existing CSPM tooling. I also help with the cost and governance review.
04 Expected deliverables
- SageMaker landing-zone and VPC design
- IAM model with least-privilege boundaries
- Endpoint deployment and approval workflow
- Monitoring, drift detection and audit pipeline
- Cost and governance review