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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink. Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks.

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AWS re:Invent 2023 Amazon Redshift Sessions Recap

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Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. Learn more about the AWS zero-ETL future with newly launched AWS databases integrations with Amazon Redshift.

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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

In line with this mission, Talent.com collaborated with AWS to develop a cutting-edge job recommendation engine driven by deep learning, aimed at assisting users in advancing their careers. The solution does not require porting the feature extraction code to use PySpark, as required when using AWS Glue as the ETL solution.

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AI/ML-driven actionable insights and themes for Amazon third-party sellers using AWS

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This post presents a solution that uses a workflow and AWS AI and machine learning (ML) services to provide actionable insights based on those transcripts. We use multiple AWS AI/ML services, such as Contact Lens for Amazon Connect and Amazon SageMaker , and utilize a combined architecture.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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TigerEye (YC S22) Is Hiring a Full Stack Engineer

Hacker News

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)