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Tag: Amazon FSx for Lustre

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker | Amazon Web Services

This post is co-written with Santosh Waddi and Nanda Kishore Thatikonda from BigBasket. BigBasket is India’s largest...

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Use Snowflake as a data source to train ML models with Amazon SageMaker

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML...

Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to...

Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute...

Scaling distributed training with AWS Trainium and Amazon EKS

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess...

Improve the performance of Apache Iceberg’s metadata file operations using Amazon FSx for Lustre on Amazon EMR

Apache Iceberg is an open table format for large datasets in Amazon Simple Storage Service (Amazon S3), and provides fast query performance over large...

Run Apache Spark with Amazon EMR on EKS backed by Amazon FSx for Lustre storage

Traditionally, Spark workloads have been run on a dedicated setup like a Hadoop stack with YARN or MESOS as a resource manager. Starting from...

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