AWS re:Inforce 2019: Amazon SageMaker Model Development in a Highly Regulated Environment (SDD315)
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this session, we dive deep into the security configurations of Amazon SageMaker components, including notebooks, distributed and batch training, and hosting endpoints. We also review Vanguard’s implementation of key controls in a highly regulated environment. These include fine-grained access control, end-to-end encryption in transit, encryption at rest with AWS KMS customer-managed customer master keys (CMKs), private connectivity to all Amazon SageMaker APIs, and comprehensive audit trails for resource and data access.
Complete Title: AWS re:Inforce 2019: Securing Your Amazon SageMaker Model Development in a Highly Regulated Environment (SDD315)
– Hung Pham, Vanguard
– Ritesh Shah, Vanguard