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To build successful machine learning (ML) models, you often need datasets unique to your business. These datasets are valuable assets and need to be secured throughout every step of the ML process—including data preparation, training, validation, and inference.
However, in a typical ML project, it can take months to build a secure workflow. So, how can you accelerate projects and help organization-wide commitment to projects and your larger ML initiatives?
Access this e-book to discover an overview of the Amazon SageMaker security features that can help you meet the security requirements of ML workloads—helping you go from idea to production faster, more securely, and with a higher rate of success.