AI

Build generative AI–powered Salesforce applications with Amazon Bedrock

This post is co-authored by Daryl Martis and Darvish Shadravan from Salesforce. This is the fourth post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1 and Part 2, we show how Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data

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Transition your Amazon Forecast usage to Amazon SageMaker Canvas

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. Launched in August 2019, Forecast predates Amazon SageMaker Canvas, a popular low-code no-code AWS tool for building, customizing, and deploying ML models, including time series forecasting models. With SageMaker Canvas, you get faster

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Connect Amazon Q Business to Microsoft SharePoint Online using least privilege access controls

Amazon Q Business is the generative artificial intelligence (AI) assistant that empowers employees with your company’s knowledge and data. Microsoft SharePoint Online is used by many organizations as a secure place to store, organize, share, and access their internal data. With generative AI, employees can get answers to their questions, summarize content, or generate insights

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Improve the productivity of your customer support and project management teams using Amazon Q Business and Atlassian Jira

Effective customer support and project management are critical aspects of providing effective customer relationship management. Atlassian Jira, a platform for issue tracking and project management functions for software projects, has become an indispensable part of many organizations’ workflows to ensure success of the customer and the product. However, extracting valuable insights from the vast amount

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Amazon SageMaker inference launches faster auto scaling for generative AI models

Today, we are excited to announce a new capability in Amazon SageMaker inference that can help you reduce the time it takes for your generative artificial intelligence (AI) models to scale automatically. You can now use sub-minute metrics and significantly reduce overall scaling latency for generative AI models. With this enhancement, you can improve the

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