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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning

For customers operating in global industries, potentially translating to and from over 10 languages, this approach can prove to be operationally complex and costly. The solution proposed in this post relies on LLMs context learning capabilities and prompt engineering. You should see a noticeable increase in the quality score.

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Transforming home ownership with Amazon Transcribe Call Analytics, Amazon Comprehend, and Amazon Bedrock: Rocket Mortgage’s journey with AWS

AWS Machine Learning

This innovation has transformed client interactions and operational efficiency through the use of Amazon Transcribe Call Analytics , Amazon Comprehend , and Amazon Bedrock. To learn more, visit Amazon Transcribe Call Analytics , Amazon Comprehend , and Amazon Bedrock. Opportunities for innovation Rocket services over 2.6

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Insights in implementing production-ready solutions with generative AI

AWS Machine Learning

Standard development best practices and effective cloud operating models, like AWS Well-Architected and the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI , are key to enabling teams to spend most of their time on tasks with high business value, rather than on recurrent, manual operations.

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How to create more effective job descriptions for customer success and professional services roles

ChurnZero

Use industry-standard titles where possible. Leverage analytics and customer success metrics to track product usage, adoption rates, and customer engagement. Secondary Responsibilities: Collaborate closely with sales, marketing, product, engineering, and other teams to ensure a seamless customer journey.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

This is often referred to as platform engineering and can be neatly summarized by the mantra “You (the developer) build and test, and we (the platform engineering team) do all the rest!” The third component is the GPU cluster, which could potentially be a Ray cluster.

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Data, insights, action! Tethr introduces practical conversational intelligence for the everyday user

Tethr

Ever noticed how much of the conversational analytics space seems designed for the data scientist, the dashboard-delver, or other types of, well… numbers people? Historically, it’s been difficult to take immediate action on the information delivered by a conversational analytics platform of any stripe. That’s the Tethr difference.

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

AWS Machine Learning

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. This enables Philips ML engineers and developers to provide updates, bug fixes, and future enhancements without disrupting the entire system.