Artificial Intelligence

Category: Best Practices

Advanced fine-tuning methods on Amazon SageMaker AI

When fine-tuning ML models on AWS, you can choose the right tool for your specific needs. AWS provides a comprehensive suite of tools for data scientists, ML engineers, and business users to achieve their ML goals. AWS has built solutions to support various levels of ML sophistication, from simple SageMaker training jobs for FM fine-tuning to the power of SageMaker HyperPod for cutting-edge research. We invite you to explore these options, starting with what suits your current needs, and evolve your approach as those needs change.

Architecture diagram of the solution

Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents

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.

Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

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.

Custom evaluation dashboard showing distribution of 116 ratings across 5-point scale with prompt category filter options

Effective cross-lingual LLM evaluation with Amazon Bedrock

In this post, we demonstrate how to use the evaluation features of Amazon Bedrock to deliver reliable results across language barriers without the need for localized prompts or custom infrastructure. Through comprehensive testing and analysis, we share practical strategies to help reduce the cost and complexity of multilingual evaluation while maintaining high standards across global large language model (LLM) deployments.

Advancing AI agent governance with Boomi and AWS: A unified approach to observability and compliance

In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower.

Build AWS architecture diagrams using Amazon Q CLI and MCP

In this post, we explore how to use Amazon Q Developer CLI with the AWS Diagram MCP and the AWS Documentation MCP servers to create sophisticated architecture diagrams that follow AWS best practices. We discuss techniques for basic diagrams and real-world diagrams, with detailed examples and step-by-step instructions.

Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.

Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use

We demonstrate two methods for generating structured responses with Amazon Bedrock: Prompt Engineering and Tool Use with the Converse API. Prompt Engineering is flexible, works with Bedrock models (including those without Tool Use support), and handles various schema types (e.g., Open API schemas), making it a great starting point. Tool Use offers greater reliability, consistent results, seamless API integration, and runtime validation of JSON schema for enhanced control.