Category: Amazon Machine Learning

The AWS Social Responsibility & Impact (SRI) team recognized an opportunity to augment this function using generative AI. The team developed an innovative solution to streamline grant proposal review and evaluation by using the natural language processing (NLP) capabilities of Amazon Bedrock. In this post, we explore the technical implementation details and key learnings from the team’s Amazon Bedrock powered grant proposal review solution, providing a blueprint for organizations seeking to optimize their grants management processes.

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.

In this post, we demonstrate how to deploy distilled versions of DeepSeek-R1 models using Amazon Bedrock Custom Model Import. We focus on importing the variants currently supported DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B, which offer an optimal balance between performance and resource efficiency.

As generative AI adoption grows, organizations should establish a generative AI operating model. An operating model defines the organizational design, core processes, technologies, roles and responsibilities, governance structures, and financial models that drive a business’s operations. In this post, we evaluate different generative AI operating model architectures that could be adopted.

RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLM’s knowledge, and generates responses with more human-like language. However, in order for generative AI to understand your data, some amount of data preparation is required, which involves a big learning curve. In this post, we walk you through how to convert your existing Aurora data into an index without needing data preparation for Amazon Kendra to perform data search and implement RAG that combines your data along with LLM knowledge to produce accurate responses.

In this post, we explore how Amazon Bedrock latency-optimized inference can help address the challenges of maintaining responsiveness in LLM applications. We’ll dive deep into strategies for optimizing application performance and improving user experience. Whether you’re building a new AI application or optimizing an existing one, you’ll find practical guidance on both the technical aspects of latency optimization and real-world implementation approaches. We begin by explaining latency in LLM applications.

Amazon has introduced two new creative content generation models on Amazon Bedrock: Amazon Nova Canvas for image generation and Amazon Nova Reel for video creation. These models transform text and image inputs into custom visuals, opening up creative opportunities for both professional and personal projects. Nova Canvas, a state-of-the-art image generation model, creates professional-grade images […]

In this post, we implemented secure fine-tuning jobs in Amazon Bedrock, which is crucial for protecting sensitive data and maintaining the integrity of your AI models. By following the best practices outlined in this post, including proper IAM role configuration, encryption at rest and in transit, and network isolation, you can significantly enhance the security posture of your fine-tuning processes.

In this post, we show you an example of a generative AI assistant application and demonstrate how to assess its security posture using the OWASP Top 10 for Large Language Model Applications, as well as how to apply mitigations for common threats.

In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.