Artificial Intelligence
Author: Adewale Akinfaderin
Adewale is a Sr. Data Scientist–Generative AI, Amazon Bedrock, where he contributes to cutting edge innovations in foundational models and generative AI applications at AWS. His expertise is in reproducible and end-to-end AI/ML methods, practical implementations, and helping global customers formulate and develop scalable solutions to interdisciplinary problems. He has two graduate degrees in physics and a doctorate in engineering.
Build reliable AI systems with Automated Reasoning on Amazon Bedrock – Part 1
Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at […]
Evaluate models or RAG systems using Amazon Bedrock Evaluations – Now generally available
Today, we’re excited to announce the general availability of these evaluation features in Amazon Bedrock Evaluations, along with significant enhancements that make them fully environment-agnostic. In this post, we explore these new features in detail, showing you how to evaluate both RAG systems and models with practical examples. We demonstrate how to use the comparison capabilities to benchmark different implementations and make data-driven decisions about your AI deployments.
Minimize generative AI hallucinations with Amazon Bedrock Automated Reasoning checks
To improve factual accuracy of large language model (LLM) responses, AWS announced Amazon Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. In this post, we discuss how to help prevent generative AI hallucinations using Amazon Bedrock Automated Reasoning checks.
Evaluate healthcare generative AI applications using LLM-as-a-judge on AWS
In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.
LLM-as-a-judge on Amazon Bedrock Model Evaluation
This blog post explores LLM-as-a-judge on Amazon Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.
Automatically generate impressions from findings in radiology reports using generative AI on AWS
This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. There are significant benefits to using a pre-trained model. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch.