AWS Machine Learning Blog
Category: Generative AI
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, which is used to further improve the model performance by aligning human preference through reinforcement learning (RLHF/RLAIF).
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.
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) derivatives that are traded directly between two parties, without going through a formal exchange.
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.
Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock
In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM. Additionally, we provide concrete evaluation results that showcase the power of synthetic data in fine-tuning when data is scarce.
Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI
Researchers developed Medusa, a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously. This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test. Medusa-1 achieves an inference speedup of around two times without sacrificing model quality, with the exact improvement varying based on model size and data used. In this post, we demonstrate its effectiveness with a 1.8 times speedup observed on a sample dataset.
LLM-as-a-judge on Amazon Bedrock Model Evaluation
This blog post explores LLM-as-a-judge on Amazon Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.
Building a virtual meteorologist using Amazon Bedrock Agents
In this post, we present a streamlined approach to deploying an AI-powered agent by combining Amazon Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.
Amazon Q Business simplifies integration of enterprise knowledge bases at scale
In this post, we demonstrate how to build a knowledge base solution by integrating enterprise data with Amazon Q Business using Amazon S3. This approach helps organizations improve operational efficiency, reduce response times, and gain valuable insights from their historical data. The solution uses AWS security best practices to promote data protection while enabling teams to create a comprehensive knowledge base from various data sources.
Transforming credit decisions using generative AI with Rich Data Co and AWS
The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. In this post, we discuss how RDC uses generative AI on Amazon Bedrock to build these assistants and accelerate its overall mission of democratizing access to sustainable credit.