Qingwei Li

The security, data privacy, governance, and regulatory controls in Amazon Bedrock can help safeguard generative AI applications. Learn how Amazon Bedrock security and data privacy measures work, including the architecture and implementation patterns.

Releasing new ML models can pose some risk of failure or poor performance because the changes are not put to the real test until they are live in the production environment. In this session, learn how to use Amazon SageMaker shadow testing to validate the performance of new ML models by comparing them against current models in production.

Deep learning (DL) projects often require integrating custom libraries with popular open-source frameworks such as TensorFlow and PyTorch. Setting up, managing, and scaling custom ML environments can be time-consuming and cumbersome even for experts.