Category: Generative AI

Although batch inference offers numerous benefits, it’s limited to 10 batch inference jobs submitted per model per Region. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. This post guides you through implementing a queue management system that automatically monitors available job slots and submits new jobs as slots become available.

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime) to a centralized video insights and summarization engine. This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. The solution notes the logged actions per individual and provides suggested actions for the uploader. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

In this post, we explore how Planview was able to develop a generative AI assistant to address complex work management process by adopting Amazon Bedrock.

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

In this post, we learn how SnapLogic’s Agent Creator leverages Amazon Bedrock to provide a low-code platform that enables enterprises to quickly develop and deploy powerful generative AI applications without deep technical expertise.

In this post, we demonstrate how to use Amazon Bedrock to create synthetic data, fine-tune a BAAI General Embeddings (BGE) model, and deploy it using Amazon SageMaker.

In this post, we show you how to unlock powerful post-call analytics and visualizations, empowering your organization to make data-driven decisions and drive continuous improvement.

In this post, we demonstrate how to deploy a contextual AI assistant. We build a solution which provides users with a familiar and convenient interface using Amazon Bedrock Knowledge Bases, Amazon Lex, and Amazon Connect, with WhatsApp as the channel.

In this post, we explore how organizations can cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. We discuss how these powerful tools enable organizations to optimize compute resources and reduce the complexity of model training and fine-tuning. We explore how you can make an informed decision about which Amazon SageMaker service is most applicable to your business needs and requirements.

In this post, we will show you how to set up an automated LLM customization (fine-tuning) workflow so that the Llama 3.x models from Meta can provide a high-quality summary of SEC filings for financial applications. Fine-tuning allows you to configure LLMs to achieve improved performance on your domain-specific tasks.