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1. How Rocket Companies modernized their data science solution on AWS

   In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

2. AWS and DXC collaborate to deliver customizable, near real-time voice-to-voice translation capabilities for Amazon Connect

   In this post, we discuss how AWS and DXC used Amazon Connect and other AWS AI services to deliver near real-time V2V translation capabilities.

3. Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

   This intelligent document processing solution uses Amazon Bedrock FMs to orchestrate a sophisticated workflow for handling multi-page healthcare documents with mixed content types. The solution uses the FM’s tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform...

4. Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

   This post introduces a solution to reduce hallucinations in Large Language Models (LLMs) by implementing a verified semantic cache using Amazon Bedrock Knowledge Bases, which checks if user questions match curated and verified responses before generating new answers. The solution combines the flexibility of LLMs with reliable, verified answers to improve response accuracy, reduce latency, and...

5. LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

   In this post, we present the continuous self-instruct fine-tuning framework as a compound AI system implemented by the DSPy framework. The framework first generates a synthetic dataset from the domain knowledge base and documents for self-instruction, then drives model fine-tuning through SFT, and introduces the human-in-the-loop workflow to collect human and AI feedback to the model response,...

6. Maximize your file server data’s potential by using Amazon Q Business on Amazon FSx for Windows

   In this post, we show you how to connect Amazon Q, a generative AI-powered assistant, to Amazon FSx for Windows File Server to securely analyze, query, and extract insights from your file system data.

7. Generate synthetic counterparty (CR) risk data with generative AI using Amazon Bedrock LLMs and RAG

   In this post, we explore how you can use LLMs with advanced Retrieval Augmented Generation (RAG) to generate high-quality synthetic data for a finance domain use case. You can use the same technique for synthetic data for other business domain use cases as well. For this post, we demonstrate how to generate counterparty risk (CR) data, which would be beneficial for over-the-counter (OTC)...

8. Turbocharging premium audit capabilities with the power of generative AI: Verisk’s journey toward a sophisticated conversational chat platform to enhance customer support

   Verisk’s Premium Audit Advisory Service is the leading source of technical information and training for premium auditors and underwriters. In this post, we describe the development of the customer support process in PAAS, incorporating generative AI, the data, the architecture, and the evaluation of the results. Conversational AI assistants are rapidly transforming customer and employee support.

9. Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

   In this post, we explore how Automated Reasoning checks work through various common FSI scenarios such as insurance legal triaging, underwriting rules validation, and claims processing.

10. Best practices for Amazon SageMaker HyperPod task governance

    In this post, we provide best practices to maximize the value of SageMaker HyperPod task governance and make the administration and data science experiences seamless. We also discuss common governance scenarios when administering and running generative AI development tasks.