Best Practices

This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.

This blog post provides a comprehensive guide to implementing robust safety protections for DeepSeek-R1 and other open weight models using Amazon Bedrock Guardrails. By following this guide, you'll learn how to use the advanced capabilities of DeepSeek models while maintaining strong security controls and promoting ethical AI practices.

By leveraging the generative AI capabilities and tooling of Amazon Bedrock, you can create an intelligent nerve center that connects diverse data sources, converts data into actionable insights, and creates a comprehensive plan to mitigate supply chain risks. This post walks through how Amazon Bedrock Flows connects your business systems, monitors medical device shortages, and provides mitigation strategies based on knowledge from Amazon Bedrock Knowledge Bases or data stored in Amazon S3 directly. You’ll learn how to create a system that stays ahead of supply chain risks.

As organizations navigate the complexities of the digital realm, generative AI has emerged as a transformative force, empowering enterprises to enhance productivity, streamline workflows, and drive innovation. To maximize the value of insights generated by generative AI, it is crucial to provide simple ways for users to preserve and share these insights using commonly used tools such as email. This post explores how you can integrate Amazon Q Business with Amazon SES to email conversations to specified email addresses.

Optimizing costs of generative AI applications on AWS is critical for realizing the full potential of this transformative technology. The post outlines key cost optimization pillars, including model selection and customization, token usage, inference pricing plans, and vector database considerations.