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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

This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform. The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022.

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Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

AWS Machine Learning

In this post, we explore how AWS customer Pro360 used the Amazon Comprehend custom classification API , which enables you to easily build custom text classification models using your business-specific labels without requiring you to learn machine learning (ML), to improve customer experience and reduce operational costs.

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Reduce food waste to improve sustainability and financial results in retail with Amazon Forecast

AWS Machine Learning

We can then call a Forecast API to create a dataset group and import data from the processed S3 bucket. We use the AutoPredictor API, which is also accessible through the Forecast console. We use the AutoPredictor API, which is also accessible through the Forecast console. Ray Wang is a Solutions Architect at AWS.

APIs 97
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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

Before you get started, refer to Part 1 for a high-level overview of the insurance use case with IDP and details about the data capture and classification stages. In Part 1, we saw how to use Amazon Textract APIs to extract information like forms and tables from documents, and how to analyze invoices and identity documents.

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Identify rooftop solar panels from satellite imagery using Amazon Rekognition Custom Labels

AWS Machine Learning

To test the model output, we use a Jupyter notebook to run Python code to detect custom labels in a supplied image by calling Amazon Rekognition APIs. The solution workflow is as follows: Store satellite imagery data in Amazon S3 as the input source. Store satellite imagery data in Amazon S3 as an input source.

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

AWS Machine Learning

Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics. Solution overview.

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Detect anomalies in manufacturing data using Amazon SageMaker Canvas

AWS Machine Learning

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. The predicted value indicates the expected value for our target metric based on the training data.

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