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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning - AI

This enables sales teams to interact with our internal sales enablement collateral, including sales plays and first-call decks, as well as customer references, customer- and field-facing incentive programs, and content on the AWS website, including blog posts and service documentation.

AWS 99
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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

These powerful frameworks simplify the complexities of parallel processing, enabling you to write code in a familiar syntax while the underlying engine manages data partitioning, task distribution, and fault tolerance. collect() Next, you can visualize the size of each document to understand the volume of data you’re processing.

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10 ways AI can make IT more productive

CIO

“The steps to create employee credentials and access permissions, pre-configure security settings, and prepare the individual for a productive first day on the job really doesn’t require human intervention,” he adds. He notes that AI can also automatically create pull requests and integrate with project management software.

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

AWS Machine Learning - AI

Users such as support engineers, project managers, and product managers need to be able to ask questions about a project, issue, or customer in order to provide excellence in their support for customers’ needs. Types of documents In Amazon Q Business, a document is a unit of data.

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A Comprehensive Guide to Communication Assessment for Recruiters

Hacker Earth Developers Blog

Use multi-modal assessments Evaluate communication across multiple channels: Written tests: Assess clarity and grammar with email or documentation tasks. For example: A software engineer could be asked to explain a technical concept to a non-technical stakeholder. A product manager might outline a roadmap to a cross-functional team.

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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning - AI

To learn more about Container Caching and other SageMaker features for inference, refer to Amazon SageMaker Documentation or check out our GitHub repositories for examples and tutorials on deploying models for inference. Software Dev Engineer with the SageMaker Inference team. About the Authors Wenzhao Sun , PhD, is a Sr.

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Update on Software Engineering Career Ladder

James Shore

As we’ve put it into practice, it’s definitely been a lot of work for managers to fill out the spreadsheets. For managers that know their employees well, the work’s been tolerable. mostly a matter of documenting what they already know. For managers that are new to their team, it’s been tough.