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To build successful machine learning models, you often need datasets unique
to your business. These datasets are extremely valuable assets and need to
be secured throughout every step of the machine learning process—including
data preparation, training, validation, and inference.
In a typical machine learning project, it can take months to build a secure
workflow before you can begin any work on your models. Maintaining
executive buy-in means delivering fast results—so anything that shortens
security-related delays will help ensure organization-wide commitment to your
project and your larger machine learning initiatives.
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 the following, we provide an overview of the
Amazon SageMaker security features that can help your organization meet
the strict security requirements of machine learning workloads—ultimately
helping you go from idea to production faster, more securely, and with a
higher rate of success.
How security helps deliver
machine learning results
