Build Secure Machine Learning Environment with AWS SageMaker

Cloud security at AWS is the highest priority. At AWS, building a secure environment from our data centers to our network architecture is of paramount importance. The same principles apply to machine learning where we provide a secure environment using our machine learning services and in particular Amazon SageMaker. In this tech talk, we will discuss how the features of Amazon SageMaker can be applied to build a secure ML environment, including the secure end points, the logging controls, the governance, and the compliance aspects. The talk will also cover the interaction of SageMaker with other AWS services to ensure the highest security for your machine learning models.

Learning Objectives:
– Learn about building a secure machine learning environment using Amazon SageMaker
– Learn about the interaction of Amazon SageMaker with other AWS services to provide the highest cloud security
– Learn the features of Amazon SageMaker that help build robust and secure machine learning models

To build successful machine learning models you need datasets unique to your organization. These datasets are extremely valuable assets and need to be secured throughout every step of the machine learning process. In a typical machine learning project it can take months to build a secure workflow before you can begin any work on your models. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly and securely. In this session, we provide an overview of the Amazon SageMaker security features that help organization meet the strict security requirements of machine learning workloads.

#machinelearning #sagemaker #cloudguru

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