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 accelerating projects by weaving security into every step of the process 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 this eBook, 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.
