Remove APIs Remove Metrics Remove Personalization
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Build a news recommender application with Amazon Personalize

AWS Machine Learning

Delivering personalized news and experiences to readers can help solve this problem, and create more engaging experiences. However, delivering truly personalized recommendations presents several key challenges: Capturing diverse user interests – News can span many topics and even within specific topics, readers can have varied interests.

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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway.

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Measure the Business Impact of Amazon Personalize Recommendations

AWS Machine Learning

We’re excited to announce that Amazon Personalize now lets you measure how your personalized recommendations can help you achieve your business goals. All customers want to track the metric that is most important for their business. All customers want to track the metric that is most important for their business.

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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105
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Build an air quality anomaly detector using Amazon Lookout for Metrics

AWS Machine Learning

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

Metrics 97
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Build a loyalty points anomaly detector using Amazon Lookout for Metrics

AWS Machine Learning

They are looking to use the loyalty program to make their customer experience more personal. This post shows you how to use an integrated solution with Amazon Lookout for Metrics to break these barriers by quickly and easily detecting anomalies in the key performance indicators (KPIs) of your interest. Open the bucket you created.

Metrics 98
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Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock

AWS Machine Learning

This requires carefully combining applications and metrics to provide complete awareness, accuracy, and control. The zAdviser uses Amazon Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data. It’s also vital to avoid focusing on irrelevant metrics or excessively tracking data.