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1. Unlock AWS Cost and Usage insights with generative AI powered by Amazon Bedrock

   In this post, we explore a solution that uses generative artificial intelligence (AI) to generate a SQL query from a user’s question in natural language. This solution can simplify the process of querying CUR data stored in an Amazon Athena database using SQL query generation, running the query on Athena, and representing it on a web portal for ease of understanding.

2. Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

   In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

3. Build ultra-low latency multimodal generative AI applications using sticky session routing in Amazon SageMaker

   In this post, we explained how the new sticky routing feature in Amazon SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models.

4. Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

   In this post, we provide a step-by-step guide for creating an enterprise ready RAG application such as a question answering bot. We use the Llama3-8B FM for text generation and the BGE Large EN v1.5 text embedding model for generating embeddings from Amazon SageMaker JumpStart.

5. Best prompting practices for using Meta Llama 3 with Amazon SageMaker JumpStart

   In this post, we dive into the best practices and techniques for prompting Meta Llama 3 using Amazon SageMaker JumpStart to generate high-quality, relevant outputs. We discuss how to use system prompts and few-shot examples, and how to optimize inference parameters, so you can get the most out of Meta Llama 3.

6. How healthcare payers and plans can empower members with generative AI

   In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. The solution presented in this post not only enhances the member experience by providing a more intuitive and user-friendly interface, but also has the potential to reduce call volumes and operational costs for healthcare payers and plans.

7. Enabling production-grade generative AI: New capabilities lower costs, streamline production, and boost security

   As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. In what we consider “Act 1” of the generative AI story, we saw previously unimaginable amounts of data and compute create models that showcase the power of generative […]

8. Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod

   In this post, we explore the journey that Thomson Reuters took to enable cutting-edge research in training domain-adapted large language models (LLMs) using Amazon SageMaker HyperPod, an Amazon Web Services (AWS) feature focused on providing purpose-built infrastructure for distributed training at scale.

9. Introducing Amazon EKS support in Amazon SageMaker HyperPod

   This post is designed for Kubernetes cluster administrators and ML scientists, providing an overview of the key features that SageMaker HyperPod introduces to facilitate large-scale model training on an EKS cluster.

10. A review of purpose-built accelerators for financial services

    In this post, we aim to provide business leaders with a non-technical overview of purpose-built accelerators (PBAs) and their role within the financial services industry (FSI).