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Why GreenOps will succeed where FinOps is failing

CIO

The result was a compromised availability architecture. A more sustainable design pattern of pilot-light or launch-on-failover would deliver both availability and cost optimization but will require greater design and implementation effort. Long-term value creation.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. This architecture workflow includes the following steps: A user submits a question through a web or mobile application. 70B and 8B.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

In this post, we describe the development journey of the generative AI companion for Mozart, the data, the architecture, and the evaluation of the pipeline. Data: Policy forms Mozart is designed to author policy forms like coverage and endorsements. The following diagram illustrates the solution architecture.

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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

AWS Machine Learning - AI

Generative AI and large language models (LLMs) offer new possibilities, although some businesses might hesitate due to concerns about consistency and adherence to company guidelines. The process of customers signing up and the solution creating personalized websites using human-curated assets and guidelines.

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Enabling privacy and choice for customers in data system design

Lacework

This article addresses privacy in the context of hosting data and considers how privacy by design can be incorporated into the data architecture. In some cases these preferences may be due to regulatory requirements, and in other cases they may be due to the customer’s own risk appetite, privacy decisions, and/or internal guidelines.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. Generative AI question-answering applications are pushing the boundaries of enterprise productivity.

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Medical content creation in the age of generative AI

AWS Machine Learning - AI

Image 1: High-level overview of the AI-assistant and its different components Architecture The overall architecture and the main steps in the content creation process are illustrated in Image 2. The solution has been designed using the following services: Amazon Elastic Container Service (ECS) : to deploy and manage our Streamlit UI.