Category: Advanced (300)

In this post you will learn how to use Spectrum to optimize resource use and shorten training times without sacrificing quality, as well as how to implement Spectrum fine-tuning with Amazon SageMaker AI training jobs. We will also discuss the tradeoff between QLoRA and Spectrum fine-tuning, showing that while QLoRA is more resource efficient, Spectrum results in higher performance overall.

This blog post introduces two major enhancements to Amazon SageMaker HyperPod that strengthen security and storage capabilities for large-scale machine learning infrastructure. The new features include customer managed key (CMK) support for encrypting EBS volumes with organization-controlled encryption keys, and Amazon EBS CSI driver integration that enables dynamic storage management for Kubernetes volumes in AI workloads.

Generative AI is transforming the way businesses interact with their customers and revolutionizing conversational interfaces for complex IT operations. Druva, a leading provider of data security solutions, is at the forefront of this transformation. In collaboration with Amazon Web Services (AWS), Druva is developing a cutting-edge generative AI-powered multi-agent copilot that aims to redefine the customer experience in data security and cyber resilience.

In this post, we demonstrate that fine-tuning VLMs provides a powerful and flexible approach to automate and significantly enhance document understanding capabilities. We also demonstrate that using focused fine-tuning allows smaller, multi-modal models to compete effectively with much larger counterparts (98% accuracy with Qwen2.5 VL 3B).

In this post, we demonstrate how Clario has used Amazon Bedrock and other AWS services to build an AI-powered solution that automates and improves the analysis of COA interviews.

In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation.

This post builds upon our previous post discussing how Clario developed an AI solution powered by Amazon Bedrock to accelerate clinical trials. Since then, Clario has further enhanced their AI capabilities, focusing on innovative solutions that streamline the generation of software configurations and artifacts for clinical trials while delivering high-quality clinical evidence.

In this post, we explore advanced cost monitoring strategies for Amazon Bedrock deployments, introducing granular custom tagging approaches for precise cost allocation and comprehensive reporting mechanisms that build upon the proactive cost management foundation established in Part 1. The solution demonstrates how to implement invocation-level tagging, application inference profiles, and integration with AWS Cost Explorer to create a complete 360-degree view of generative AI usage and expenses.

In this post, we introduce a comprehensive solution for proactively managing Amazon Bedrock inference costs through a cost sentry mechanism designed to establish and enforce token usage limits, providing organizations with a robust framework for controlling generative AI expenses. The solution uses serverless workflows and native Amazon Bedrock integration to deliver a predictable, cost-effective approach that aligns with organizational financial constraints while preventing runaway costs through leading indicators and real-time budget enforcement.

This post provides a comprehensive hands-on guide to fine-tune Amazon Nova Lite for document processing tasks, with a focus on tax form data extraction. Using our open-source GitHub repository code sample, we demonstrate the complete workflow from data preparation to model deployment.