How Secure is AWS Machine Learning?
The machine learning lifecycle is a cyclical and iterative process of continuous data ingestion and model updates, which can create complex security risks. Complying to recommended security practices at every stage of the ML workflow is crucial to ensure the security of ML applications. This blog post discusses the AWS best practices for securing ML models based on the course AWS Certified Machine Learning Associate (MLA-C01). Security is a shared responsibility between AWS and the customer, and AWS Well-Architected Framework provides architectural best practices for designing secure ML workloads on AWS. These best practices must include ML-specific security along with traditional software development security.
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