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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI. This will provision the backend infrastructure and services that the sales analytics application will rely on. You’ll use this file when setting up your function to query sales data.

APIs 107
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Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

AWS Machine Learning

Gramener’s GeoBox solution empowers users to effortlessly tap into and analyze public geospatial data through its powerful API, enabling seamless integration into existing workflows. Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020.

APIs 134
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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning

Founded in 2014, Veritone empowers people with AI-powered software and solutions for various applications, including media processing, analytics, advertising, and more. Amazon Transcribe The transcription for the entire video is generated using the StartTranscriptionJob API. The videos are then segmented into individual shots.

APIs 137
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2019 predictions for ambitious contact centres

Vonage

Nine out of the ten topics mentioned in our inaugural 2014 version have become mainstream. Open standards, cloud functionality, programmable micro services and APIs, combined with AI smarts, now offer the budding Lego builder entirely new possibilities. So, here’s hoping that form still runs true for this latest iteration.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning

You can change the configuration later from the SageMaker Canvas UI or using SageMaker APIs. About the Authors Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. He helps customers implement big data, machine learning, analytics solutions, and generative AI implementations.

Surveys 126
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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. This is a joint blog with AWS and Philips. Wioletta Stobieniecka is a Data Scientist at AWS Professional Services.

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

2014, Article ID 781670, 11 pages, 2014.). For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference. The Step Functions state machine, S3 bucket, Amazon API Gateway resources, and Lambda function codes are stored in the GitHub repo.