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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. AWS CodeBuild is a fully managed continuous integration service in the cloud.

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The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning Blog

In this post we highlight how the AWS Generative AI Innovation Center collaborated with the AWS Professional Services and PGA TOUR to develop a prototype virtual assistant using Amazon Bedrock that could enable fans to extract information about any event, player, hole or shot level details in a seamless interactive manner.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

If we were to use RAG to converse with these reports, we could ask questions such as “What are the risks that faced company X in 2022,” or “What is the net revenue of company Y in 2022?” Consider the question: “What are the top 5 companies with the highest revenue in 2022?” Sort the revenues in descending order.

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How to Build an End-to-End Energy Price Forecasting Solution with Snowflake

phData

In this blog, we’ll show you how to build a robust energy price forecasting solution within the Snowflake Data Cloud ecosystem. We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices.

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Deploy a predictive maintenance solution for airport baggage handling systems with Amazon Lookout for Equipment

AWS Machine Learning Blog

In this post, we describe how AWS Partner Airis Solutions used Amazon Lookout for Equipment , AWS Internet of Things (IoT) services, and CloudRail sensor technologies to provide a state-of-the-art solution to address these challenges. It’s an easy way to run analytics on IoT data to gain accurate insights.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

After reading a few blog posts and DJL’s official documentation, we were sure DJL would provide the best solution to our problem. It also includes support for new hardware like ARM (both in servers like AWS Graviton and laptops with Apple M1 ) and AWS Inferentia. The architecture of DJL is engine agnostic.

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What Are The Best Third-Party Data Ingestion Tools For Snowflake?

phData

Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.). Streamsets Data Collector StreamSets Data Collector Engine is an easy-to-use data pipeline engine for streaming, CDC, and batch ingestion from any source to any destination.