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A progress update on our commitment to safe, responsible generative AI

AWS Machine Learning

We’ve created more than 10 AI Service Cards thus far to deliver transparency for our customers as part of our comprehensive development process that addresses fairness, explainability, veracity and robustness, governance, transparency, privacy and security, safety, and controllability.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

Plus, learn how to evolve from data aggregation to data semantics to support data-driven applications while maintaining flexibility and governance. Learn about Amazon SageMaker tooling for model governance, bias, explainability, and monitoring, and about transparency in the form of service cards as potential risk mitigation strategies.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. Valid government-issued ID (driver’s license, passport, etc.)

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eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

AWS Machine Learning

The application’s frontend is accessible through Amazon API Gateway , using both edge and private gateways. Amazon Bedrock offers a practical environment for benchmarking and a cost-effective solution for managing workloads due to its serverless operation. The following diagram visualizes the architecture diagram and workflow.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

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The executive’s guide to generative AI for sustainability

AWS Machine Learning

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years.

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning

Furthermore, model hosting on Amazon SageMaker JumpStart can help by exposing the endpoint API without sharing model weights. FL can have a potential impact on the entire treatment cycle, and now even more so with the focus on data interoperability from large federal organizations and government leaders.