AWS Machine Learning Blog

Category: Generative AI

Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

In this post, we demonstrate how you can address the challenges of model customization being complex, time-consuming, and often expensive by using fully managed environment with Amazon SageMaker Training jobs to fine-tune the Mixtral 8x7B model using PyTorch Fully Sharded Data Parallel (FSDP) and Quantized Low Rank Adaptation (QLoRA).

Amazon SageMaker Inference now supports G6e instances

G6e instances on SageMaker unlock the ability to deploy a wide variety of open source models cost-effectively. With superior memory capacity, enhanced performance, and cost-effectiveness, these instances represent a compelling solution for organizations looking to deploy and scale their AI applications. The ability to handle larger models, support longer context lengths, and maintain high throughput makes G6e instances particularly valuable for modern AI applications.

Orchestrate generative AI workflows with Amazon Bedrock and AWS Step Functions

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. We also touch on the usage of Retrieval Augmented Generation (RAG) to optimize outputs and provide an extra layer of precision, as well as other possible integrations through Step Functions.

Build generative AI applications on Amazon Bedrock with the AWS SDK for Python (Boto3)

In this post, we demonstrate how to use Amazon Bedrock with the AWS SDK for Python (Boto3) to programmatically incorporate FMs. We explore invoking a specific FM and processing the generated text, showcasing the potential for developers to use these models in their applications for a variety of use cases

Amazon Bedrock Flows is now generally available with enhanced safety and traceability

Today, we are excited to announce the general availability of Amazon Bedrock Flows (previously known as Prompt Flows). With Bedrock Flows, you can quickly build and execute complex generative AI workflows without writing code. Bedrock Flows makes it easier for developers and businesses to harness the power of generative AI, enabling you to create more sophisticated and efficient AI-driven solutions for your customers.

Implement secure API access to your Amazon Q Business applications with IAM federation user access management

Amazon Q Business provides a rich set of APIs to perform administrative tasks and to build an AI assistant with customized user experience for your enterprise. In this post, we show how to use Amazon Q Business APIs when using AWS Identity and Access Management (IAM) federation for user access management.

Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

In this post, we show you how to implement an audio and video segmentation solution using SageMaker Ground Truth. We guide you through deploying the necessary infrastructure using AWS CloudFormation, creating an internal labeling workforce, and setting up your first labeling job. By the end of this post, you will have a fully functional audio/video segmentation workflow that you can adapt for various use cases, from training speech synthesis models to improving video generation capabilities.

Using responsible AI principles with Amazon Bedrock Batch Inference

In this post, we explore a practical, cost-effective approach for incorporating responsible AI guardrails into Amazon Bedrock Batch Inference workflows. Although we use a call center’s transcript summarization as our primary example, the methods we discuss are broadly applicable to a variety of batch inference use cases where ethical considerations and data protection are a top priority.

Architecture overview

Revolutionizing knowledge management: VW’s AI prototype journey with AWS

we’re excited to share the journey of the VW—an innovator in the automotive industry and Europe’s largest car maker—to enhance knowledge management by using generative AI, Amazon Bedrock, and Amazon Kendra to devise a solution based on Retrieval Augmented Generation (RAG) that makes internal information more easily accessible by its users. This solution efficiently handles documents that include both text and images, significantly enhancing VW’s knowledge management capabilities within their production domain.

Solution architecture

Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. In this post, we explore how you can use Amazon […]