Category: Artificial Intelligence

In this post, we explore Clearwater Analytics’ foray into generative AI, how they’ve architected their solution with Amazon SageMaker, and dive deep into how Clearwater Analytics is using LLMs to take advantage of more than 18 years of experience within the investment management domain while optimizing model cost and performance.

In this post, we demonstrate how we innovated to build a Retrieval Augmented Generation (RAG) application with agentic workflow and a knowledge base on Amazon Bedrock. We implemented the RAG pipeline in a Slack chat-based assistant to empower the Amazon Twitch ads sales team to move quickly on new sales opportunities.

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.

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy a model from ModelTrainer to a SageMaker endpoint, and provides a single interface for multiple deployment configurations.

In this post, we focus on the ModelTrainer class for simplifying the training experience. The ModelTrainer class provides significant improvements over the current Estimator class, which are discussed in detail in this post. We show you how to use the ModelTrainer class to train your ML models, which includes executing distributed training using a custom script or container. In Part 2, we show you how to build a model and deploy to a SageMaker endpoint using the improved ModelBuilder class.

In April 2024, we launched our AI sales assistant, which we call Field Advisor, making it available to AWS employees in the Sales, Marketing, and Global Services organization, powered by Amazon Q Business. Since that time, thousands of active users have asked hundreds of thousands of questions through Field Advisor, which we have embedded in our customer relationship management (CRM) system, as well as through a Slack application.

In this post, we walk you through configuring and integrating Amazon Q for Business with Aurora PostgreSQL-Compatible to enable your database administrators, data analysts, application developers, leadership, and other teams to quickly get accurate answers to their questions related to the content stored in Aurora PostgreSQL databases.

In this post, we illustrate the importance of generative AI in the collaboration between Tealium and the AWS Generative AI Innovation Center (GenAIIC) team by automating the following: 1/ Evaluating the retriever and the generated answer of a RAG system based on the Ragas Repository powered by Amazon Bedrock, 2/ Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository. An instruction refers to a general direction or command given to the model to guide generation of a response. These instructions were generated using Anthropic’s Claude on Amazon Bedrock, and 4/ Providing a UI for a human-based feedback mechanism that complements an evaluation system powered by Amazon Bedrock.

In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the power of generative AI in revolutionizing their learning assessment process. We explore the challenges faced in traditional question-answer (QA) generation and the innovative AI-driven solution developed to address them.

Today, we are excited to announce that Pixtral 12B (pixtral-12b-2409), a state-of-the-art vision language model (VLM) from Mistral AI that excels in both text-only and multimodal tasks, is available for customers through Amazon SageMaker JumpStart. You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models that can be deployed with one click for running inference. In this post, we walk through how to discover, deploy, and use the Pixtral 12B model for a variety of real-world vision use cases.