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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

In this post, we discuss best practices for working with FMEval in ground truth curation and metric interpretation for evaluating question answering applications for factual knowledge and quality. Ground truth data in AI refers to data that is known to be true, representing the expected outcome for the system being modeled.

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Gone Virtual: Recap of the CETX Conference

Callminer

While it may not have been our typical, in-person experience filled with cocktail hours and outdoor activities, there was no shortage of entertainment and powerful and engaging insight from the customer experience (CX) and contact center industry’s most influential leaders, and hands-on practitioners. The great online kick-off.

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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

She assists customers in adopting best practices while deploying solution in AWS, and is a Analytics and ML enthusiast. He works with Media and Entertainment customers and has interests in machine learning technologies. Data Engineer for Amp on Amazon. David Kuo is a Solutions Architect at Amazon Web Services.

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

She assists customers in adopting best practices while deploying solution in AWS, and is a Analytics and ML enthusiast. He works with Media and Entertainment customers and has interests in machine learning technologies. Data Engineer for Amp on Amazon. David Kuo is a Solutions Architect at Amazon Web Services.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

Solution overview Let’s imagine a scenario where a movie entertainment company promotes movies to different users via an email campaign. He supports strategic customers with AI/ML best practices cross many industries. He is deeply passionate about applying ML/DL and big data techniques to solve real-world problems.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

He has over 11 years of experience in developing and leading data science, machine learning, and big data initiatives. Currently he is helping enterprise customers modernizing their AI/ML workloads within the cloud using industry best practices. Tanvi Singhal is a Data Scientist within AWS Professional Services.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.