Amazon-Machine-Learning
5 postsIn this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.
In this post, we’ll demonstrate how to configure an Amazon Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided by Amazon Q Business to query real-time data and take actions in ServiceNow.
Amazon Bedrock Data Automation in public preview, offers a unified experience for developers of all skillsets to easily automate the extraction, transformation, and generation of relevant insights from documents, images, audio, and videos to build generative AI–powered applications. In this post, we demonstrate how to use Amazon Bedrock Data Automation in the AWS Management Console and the AWS SDK for Python (Boto3) for media analysis and intelligent document processing (IDP) workflows.
TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.
Bedrock multi-agent collaboration enables developers to build, deploy, and manage multiple specialized agents working together seamlessly to address increasingly complex business workflows. In this post, we show you how agentic workflows with Amazon Bedrock Agents can help accelerate this journey for research scientists with a natural language interface. We define an example analysis pipeline, specifically for lung cancer survival with clinical, genomics, and imaging modalities of biomarkers. We showcase a variety of specialized agents including a biomarker database analyst, statistician, clinical evidence researcher, and medical imaging expert in collaboration with a supervisor agent. We demonstrate advanced capabilities of agents for self-review and planning that help build trust with end users by breaking down complex tasks into a series of steps and showing the chain of thought to generate the final answer.