Category: Learning Levels

In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.

Discover how to build a GenAI powered virtual IT troubleshooting assistant using HAQM Q Business. This innovative solution integrates with popular ITSM tools like ServiceNow, Atlassian Jira, and Confluence to streamline information retrieval and enhance collaboration across your organization. By harnessing the power of generative AI, this assistant can significantly boost operational efficiency and provide 24/7 support tailored to individual needs. Learn how to set up, configure, and leverage this solution to transform your enterprise information management.

In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. By extracting and structuring the key information from the source materials, the models can create a searchable knowledge base that allows you to quickly locate the data, formulas, and visualizations you need to support your work.

AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of HAQM Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.

In this post, we demonstrate how to use HAQM Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.

In this post, we explore using HAQM Bedrock to create a text-to-SQL application using RAG. We use Anthropic’s Claude 3.5 Sonnet model to generate SQL queries, HAQM Titan in HAQM Bedrock for text embedding and HAQM Bedrock to access these models.

In this post, we show how SageMaker HyperPod, and its new features introduced at AWS re:Invent 2024, is designed to meet the demands of modern AI workloads, offering a persistent and optimized cluster tailored for distributed training and accelerated inference at cloud scale and attractive price-performance.

This post focuses on RAG evaluation with HAQM Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. By the end of this post, you will understand how the latest HAQM Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.

In this post, we dive deeper into one of MaestroQA’s key features—conversation analytics, which helps support teams uncover customer concerns, address points of friction, adapt support workflows, and identify areas for coaching through the use of HAQM Bedrock. We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies.

In this blog post, we showcase a curated gallery of visuals generated by Nova Canvas—categorized by real-world use cases—from marketing and product visualization to concept art and design exploration. Each image is paired with the prompt and parameters that generated it, providing a practical starting point for your own AI-driven creativity. Whether you’re crafting specific types of images, optimizing workflows, or simply seeking inspiration, this guide will help you unlock the full potential of HAQM Nova Canvas.