Category: Learning Levels

In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. In this post, we explore how you can use Amazon […]

In this post, we illustrate automating the responses to email inquiries by using Amazon Bedrock Knowledge Bases and Amazon Simple Email Service (Amazon SES), both fully managed services. By linking user queries to relevant company domain information, Amazon Bedrock Knowledge Bases offers personalized responses.

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.

In this post, we guide you through the phases of customizing SLMs on AWS, with a specific focus on automotive terminology for diagnostics as a Q&A task. We begin with the data analysis phase and progress through the end-to-end process, covering fine-tuning, deployment, and evaluation. We compare a customized SLM with a general purpose LLM, using various metrics to assess vocabulary richness and overall accuracy.

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.

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.

This post focuses on doing RAG on heterogeneous data formats. We first introduce routers, and how they can help managing diverse data sources. We then give tips on how to handle tabular data and will conclude with multimodal RAG, focusing specifically on solutions that handle both text and image data.

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.