Artificial Intelligence
Author: Sharon Li
Sharon Li is a solutions architect at AWS, based in the Boston, MA area. She works with enterprise customers helping them solve difficult problems and build on AWS. Outside of work, she likes to spend time with her family and explore local restaurants.
Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference
This post provides a comprehensive hands-on guide to fine-tune Amazon Nova Lite for document processing tasks, with a focus on tax form data extraction. Using our open-source GitHub repository code sample, we demonstrate the complete workflow from data preparation to model deployment.
Effectively use prompt caching on Amazon Bedrock
Prompt caching, now generally available on Amazon Bedrock with Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, along with Nova Micro, Nova Lite, and Nova Pro models, lowers response latency by up to 85% and reduces costs up to 90% by caching frequently used prompts across multiple API calls. This post provides a detailed overview of the prompt caching feature on Amazon Bedrock and offers guidance on how to effectively use this feature to achieve improved latency and cost savings.
Evaluate conversational AI agents with Amazon Bedrock
As conversational artificial intelligence (AI) agents gain traction across industries, providing reliability and consistency is crucial for delivering seamless and trustworthy user experiences. However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that […]
Improve scalability for Amazon Rekognition stateless APIs using multiple regions
In previous blog post, we described an end-to-end identity verification solution in a single AWS Region. The solution uses the Amazon Rekognition APIs DetectFaces for face detection and CompareFaces for face comparison. We think of those APIs as stateless APIs because they don’t depend on an Amazon Rekognition face collection. They’re also idempotent, meaning repeated […]