Category: Intermediate (200)

Amazon Finance Automation developed a large language model (LLM)-based question-answer chat assistant on Amazon Bedrock. This solution empowers analysts to rapidly retrieve answers to customer queries, generating prompt responses within the same communication thread. As a result, it drastically reduces the time required to address customer queries. In this post, we share how Amazon Finance Automation built this generative AI Q&A chat assistant using Amazon Bedrock.

For enterprises in the realm of cloud computing and software development, providing secure code repositories is essential. As sophisticated cybersecurity threats become more prevalent, organizations must adopt proactive measures to protect their assets. Amazon Bedrock offers a powerful solution by automating the process of scanning repositories for vulnerabilities and remediating them. This post explores how you can use Amazon Bedrock to enhance the security of your repositories and maintain compliance with organizational and regulatory standards.

This post explores how 123RF used Amazon Bedrock, Anthropic’s Claude 3 Haiku, and a vector store to efficiently translate content metadata, significantly reduce costs, and improve their global content discovery capabilities.

In this post, we explore a practical, cost-effective approach for incorporating responsible AI guardrails into Amazon Bedrock Batch Inference workflows. Although we use a call center’s transcript summarization as our primary example, the methods we discuss are broadly applicable to a variety of batch inference use cases where responsible considerations and data protection are a top priority.

In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. This approach can also enhance the quality of retrieved information and responses generated by the RAG applications.

MSD, a leading pharmaceutical company, collaborates with AWS to implement a powerful text-to-SQL generative AI solution using Amazon Bedrock and Anthropic’s Claude 3.5 Sonnet model. This approach streamlines data extraction from complex healthcare databases like DE-SynPUF, enabling analysts to generate SQL queries from natural language questions. The solution addresses challenges such as coded columns, non-intuitive names, and ambiguous queries, significantly reducing query time and democratizing data access.

In this post, we introduce the core dimensions of responsible AI and explore considerations and strategies on how to address these dimensions for Amazon Bedrock applications.

The Cohere Embed multimodal embeddings model is now generally available on Amazon SageMaker JumpStart. This model is the newest Cohere Embed 3 model, which is now multimodal and capable of generating embeddings from both text and images, enabling enterprises to unlock real value from their vast amounts of data that exist in image form. In this post, we discuss the benefits and capabilities of this new model with some examples.

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM), making it easier to securely share and discover machine learning (ML) models across your AWS accounts. In this post, we will show you how to use this new cross-account model sharing feature to build your own centralized model governance capability, which is often needed for centralized model approval, deployment, auditing, and monitoring workflows.

This post explores a solution that uses the power of AWS generative AI capabilities like Amazon Bedrock and OpenSearch vector search to perform damage appraisals for insurers, repair shops, and fleet managers.