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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. We specifically focus on SageMaker with MLflow.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

AWS Machine Learning

However, there are benefits to building an FM-based classifier using an API service such as Amazon Bedrock, such as the speed to develop the system, the ability to switch between models, rapid experimentation for prompt engineering iterations, and the extensibility into other related classification tasks.

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How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

AWS Machine Learning

In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets. The following diagram illustrates the solution architecture.

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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

They provide access to external data and APIs or enable specific actions and computation. To improve accuracy, we tested model fine-tuning, training the model on common queries and context (such as database schemas and their definitions). At RDC, Hendra designs end-to-end analytics solutions within an Agile DevOps framework.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

Designing the prompt Before starting any scaled use of generative AI, you should have the following in place: A clear definition of the problem you are trying to solve along with the end goal. Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. client = boto3.client("bedrock-runtime",

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

We also look into how to further use the extracted structured information from claims data to get insights using AWS Analytics and visualization services. We highlight on how extracted structured data from IDP can help against fraudulent claims using AWS Analytics services. Amazon Redshift is another service in the Analytics stack.