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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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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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Maximizing ROI with CPQ: 10 Best Practices for Sales Success

Cincom

It demands a well-defined framework that integrates automation, pricing governance, and seamless CRM and ERP connectivityall of which are essential for driving predictable revenue and operational efficiency. Use APIs and middleware to bridge gaps between CPQ and existing enterprise systems, ensuring smooth data flow.

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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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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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How to Successfully Implement Customer Journey Analytics – Part 1

Pointillist

Pointillist can handle data in all forms, whether it is in tables, excel files, server logs, or 3rd party APIs. 3rd Party APIs: Pointillist has a large number of connectors using 3rd party APIs. Governance. Raw data can be sent directly to Pointillist without requiring aggregations or roll-ups of any kind. To Summarize.

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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.