AWS Machine Learning Blog
Category: Intermediate (200)
Integrating custom dependencies in Amazon SageMaker Canvas workflows
When implementing machine learning workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an example of how to incorporate code that relies on external dependencies into your SageMaker Canvas workflows.
Amazon Bedrock launches Session Management APIs for generative AI applications (Preview)
Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across […]
Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference
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.
Build a generative AI enabled virtual IT troubleshooting assistant using Amazon Q Business
Discover how to build a GenAI powered virtual IT troubleshooting assistant using Amazon 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.
Streamline AWS resource troubleshooting with Amazon Bedrock Agents and AWS Support Automation Workflows
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 Amazon Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.
Create generative AI agents that interact with your companies’ systems in a few clicks using Amazon Bedrock in Amazon SageMaker Unified Studio
In this post, we demonstrate how to use Amazon Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.
Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)
In this post, we explore using Amazon Bedrock to create a text-to-SQL application using RAG. We use Anthropic’s Claude 3.5 Sonnet model to generate SQL queries, Amazon Titan in Amazon Bedrock for text embedding and Amazon Bedrock to access these models.
Unleash AI innovation with Amazon SageMaker HyperPod
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.
Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight
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 Amazon Bedrock. We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies.
Exploring creative possibilities: A visual guide to Amazon Nova Canvas
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 Amazon Nova Canvas.