Category: Generative AI

n this post, we create a virtual analyst that can answer natural language queries of stocks matching certain technical indicator criteria using Amazon Bedrock Agents.

This post presents a strategy for optimizing LLM-based applications. Given the increasing need for efficient and cost-effective AI solutions, we present a serverless read-through caching blueprint that uses repeated data patterns. With this cache, developers can effectively save and access similar prompts, thereby enhancing their systems’ efficiency and response times.

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).

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.

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.

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

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.

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.

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.

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 responsible considerations and data protection are a top priority.