Category: Learning Levels

In this post, we focus on building a Text-to-SQL solution with Amazon Bedrock, a managed service for building generative AI applications. Specifically, we demonstrate the capabilities of Amazon Bedrock Agents. Part 2 explains how we extended the solution to provide business insights using Amazon Q in QuickSight, a business intelligence assistant that answers questions with auto-generated visualizations.

In this post, we discuss permission management strategies, focusing on attribute-based access control (ABAC) patterns that enable granular user access control while minimizing the proliferation of AWS Identity and Access Management (IAM) roles. We also share proven best practices that help organizations maintain security and compliance without sacrificing operational efficiency in their ML workflows.

In this post, we explore how SageMaker and federated learning help financial institutions build scalable, privacy-first fraud detection systems.

In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for voice and multimodal conversational AI agents to build applications with human-like conversational AI. You learned about common use cases of voice agents and the cascaded models approach, where you orchestrate several components to build your voice AI agent. In this post (Part 2), you explore how to use speech-to-speech foundation model, Amazon Nova Sonic, and the benefits of using a unified model.

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

With a one-click installation of the Amazon Elastic Kubernetes Service (Amazon EKS) add-on for SageMaker HyperPod observability, you can consolidate health and performance data from NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. In this post, we walk you through installing and using the unified dashboards of the out-of-the-box observability feature in SageMaker HyperPod. We cover the one-click installation from the Amazon SageMaker AI console, navigating the dashboard and metrics it consolidates, and advanced topics such as setting up custom alerts.

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker Studio. In this post, we show you how to remotely connect your local VS Code to SageMaker Studio development environments to use your customized development environment while accessing Amazon SageMaker AI compute resources.

This post demonstrates building an intelligent AI assistant using Mistral AI models on AWS and MCP, integrating real-time location services, time data, and contextual memory to handle complex multimodal queries. This use case, restaurant recommendations, serves as an example, but this extensible framework can be adapted for enterprise use cases by modifying MCP server configurations to connect with your specific data sources and business systems.

Amazon Q Business for Retail Intelligence is an AI-powered assistant designed to help retail businesses streamline operations, improve customer service, and enhance decision-making processes. This solution is specifically engineered to be scalable and adaptable to businesses of various sizes, helping them compete more effectively. In this post, we show how you can use Amazon Q Business for Retail Intelligence to transform your data into actionable insights.

The business team in Parcel Perform often needs access to data to answer questions related to merchants’ parcel deliveries, such as “Did we see a spike in delivery delays last week? If so, in which transit facilities were this observed, and what was the primary cause of the issue?” Previously, the data team had to manually form the query and run it to fetch the data. With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. In this post, we discuss how Parcel Perform incorporated generative AI, data storage, and data access through AWS services to make timely decisions.