Tag: AI

In this post, we discuss how GoDaddy’s Care & Services team, in close collaboration with the  AWS GenAI Labs team, built Lighthouse—a generative AI solution powered by Amazon Bedrock. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. With Amazon Bedrock, GoDaddy’s Lighthouse mines insights from customer care interactions using crafted prompts to identify top call drivers and reduce friction points in customers’ product and website experiences, leading to improved customer experience.

In this post, we showcase how to fine-tune a text and vision model, such as Meta Llama 3.2, to better perform at visual question answering tasks. The Meta Llama 3.2 Vision Instruct models demonstrated impressive performance on the challenging DocVQA benchmark for visual question answering. By using the power of Amazon SageMaker JumpStart, we demonstrate the process of adapting these generative AI models to excel at understanding and responding to natural language questions about images.

In this post, we explore how Principal used QnABot paired with Amazon Q Business and Amazon Bedrock to create Principal AI Generative Experience: a user-friendly, secure internal chatbot for faster access to information. Using generative AI, Principal’s employees can now focus on deeper human judgment based decisioning, instead of spending time scouring for answers from data sources manually.

Cloud costs can significantly impact your business operations. Gaining real-time visibility into infrastructure expenses, usage patterns, and cost drivers is essential. To allocate costs to cloud resources, a tagging strategy is essential. This post outlines steps you can take to implement a comprehensive tagging governance strategy across accounts, using AWS tools and services that provide visibility and control. By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment.

In this post, we walk through a step-by-step guide to automating invoice processing using Streamlit and Amazon Bedrock, addressing the challenge of handling invoices from multiple vendors with different formats. We show how to set up the environment, process invoices stored in Amazon S3, and deploy a user-friendly Streamlit application to review and interact with the processed data.