Category: Technical How-to

In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we explain in detail the fine-tuning process, hosting fine-tuned models with provisioned throughput, and using the fine-tuned Amazon Nova models for inference.

In this post, we introduced the Open Source Bedrock Agent Evaluation framework, a Langfuse-integrated solution that streamlines the agent development process. We demonstrated how this evaluation framework can be integrated with pharmaceutical research agents. We used it to evaluate agent performance against biomarker questions and sent traces to Langfuse to view evaluation metrics across question types.

In this post, the AWS and Cisco teams unveil a new methodical approach that addresses the challenges of enterprise-grade SQL generation. The teams were able to reduce the complexity of the NL2SQL process while delivering higher accuracy and better overall performance.

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

In this post, we explore how Low-Rank Adaptation (LoRA) can be used to address these challenges effectively. Specifically, we discuss using LoRA serving with LoRA eXchange (LoRAX) and Amazon Elastic Compute Cloud (Amazon EC2) GPU instances, allowing organizations to efficiently manage and serve their growing portfolio of fine-tuned models, optimize costs, and provide seamless performance for their customers.

This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We’ll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.

In this post, we demonstrate how to elevate business productivity by leveraging Amazon Q to provide insights that enable research, data analysis, and report potential fraud cases within Amazon Connect.

This post demonstrates how to integrate open-source multi-agent framework, LangGraph, with Amazon Bedrock. It explains how to use LangGraph and Amazon Bedrock to build powerful, interactive multi-agent applications that use graph-based orchestration.

This post demonstrates how enterprises can implement a scalable agentic text-to-SQL solution using Amazon Bedrock Agents, with advanced error-handling tools and automated schema discovery to enhance database query efficiency.

In this post, we demonstrate how you can use custom plugins for Amazon Q Business to build a chatbot that can interact with multiple APIs using natural language prompts. We showcase how to build an AIOps chatbot that enables users to interact with their AWS infrastructure through natural language queries and commands. The chatbot is capable of handling tasks such as querying the data about Amazon Elastic Compute Cloud (Amazon EC2) ports and Amazon Simple Storage Service (Amazon S3) buckets access settings.