Generative Data Intelligence

Tag: Amazon Machine Learning

How RallyPoint and AWS are personalizing job recommendations to help military veterans and service providers transition back into civilian life using Amazon Personalize

This post was co-written with Dave Gowel, CEO of RallyPoint. In his own words, “RallyPoint is an online social and professional network for veterans,...

Predicting new and existing product sales in semiconductors using Amazon Forecast

This is a joint post by NXP SEMICONDUCTORS N.V. & AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a...

HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually

This is a guest post by Neslihan Erdogan, Global Industrial IT Manager at HAYAT HOLDING. With the ongoing digitization of the manufacturing processes and...

Using Amazon SageMaker with Point Clouds: Part 1- Ground Truth for 3D labeling

In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. In part 1, we discuss the dataset...

Real-time fraud detection using AWS serverless and machine learning services

Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers,...

Training large language models on Amazon SageMaker: Best practices

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models...

How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

This post is co-written with Suhyoung Kim, General Manager at KakaoGames Data Analytics Lab. Kakao Games is a top video game publisher and developer...

Build a GNN-based real-time fraud detection solution using the Deep Graph Library without using external graph storage

Fraud detection is an important problem that has applications in financial services, social media, ecommerce, gaming, and other industries. This post presents an implementation...

Identifying defense coverage schemes in NFL’s Next Gen Stats

This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers...

Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Today, the NFL is continuing their journey to increase the number of statistics provided by the Next Gen Stats Platform to all 32 teams...

Amazon SageMaker built-in LightGBM now offers distributed training using Dask

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get...

How to redact PII data in conversation transcripts

Customer service interactions often contain personally identifiable information (PII) such as names, phone numbers, and dates of birth. As organizations incorporate machine learning (ML)...

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