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Build ML environment for regulatory customers

Talkat Conf42 Machine Learning 2021 · on Thursday, 2021-07-29· [machine-learning, security]
slides:none on file

Regulated customers (banking, healthcare, pharma) get a hardened ML platform using AWS Service Catalog and SageMaker Studio: service catalog products shared across accounts, a centralized provisioning pipeline, and enforcement of private VPC with no internet. Controls include customer KMS keys, encryption in transit and at rest, disabled notebook root access, imported security groups, and Service Control Policies that block data exfiltration and opt data out of model improvement. Monitoring flags noncompliant resources and the setup enforces policy at scale.

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