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AWS Machine Learning

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AWS Machine Learning

29.03.2025

Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today | AWS Machine Learning Blog

Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. In this post, we discuss how to get started with image content filters in Amazon Bedrock Guardrails.

AWS Machine Learning

29.03.2025

Enhance governance with metadata enforcement rules in Amazon SageMaker | AWS Big Data Blog

Amazon SageMaker Catalog now supports metadata rules allowing organizations to enforce metadata standards across data publishing and subscription workflows. In this post, we guide you through two workflows: setting up metadata enforcement rules for a specific domain and publishing an asset or data product in a catalog, and setting up metadata enforcement rules for a specific domain and subscribing to an asset or data product that is owned by a project within that domain.

AWS Machine Learning

01.04.2025

Build agentic systems with CrewAI and Amazon Bedrock | AWS Machine Learning Blog

In this post, we explore how CrewAI’s open source agentic framework, combined with Amazon Bedrock, enables the creation of sophisticated multi-agent systems that can transform how businesses operate. Through practical examples and implementation details, we demonstrate how to build, deploy, and orchestrate AI agents that can tackle complex tasks with minimal human oversight.

AWS Machine Learning

01.04.2025

Optimizing cost savings: The advantage of Amazon Aurora over self-managed open source databases | AWS Database Blog

In this post, we highlight often overlooked architectural designs and the inherent features of Aurora that optimize costs when deploying an open source database. The following sections examine various use cases, contrasting typical self-managed database configurations and their associated costs with the equivalent solution on Aurora, highlighting potential cost savings and operational efficiencies.

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