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By leveraging Alluxio Distributed Cache, RedNote eliminated storage bottlenecks causing model training time to exceed SLAs, accelerated cross-cloud model distribution, and lowered model distribution costs

Publicly traded, top 10 global e-commerce company leverages Alluxio Enterprise AI to accelerates training of search and recommendation AI Model with Alluxio and cut AWS S3 API and egress charges by over 50%.

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We’re excited to introduce Rapid Alluxio Deployer (RAD) on AWS, which allows you to experience the performance benefits of Alluxio in less than 30 minutes. RAD is designed with a split-plane architecture, which ensures that your data remains secure within your AWS environment, giving you peace of mind while leveraging Alluxio’s capabilities.

PyTorch is one of the most popular deep learning frameworks in production today. As models become increasingly complex and dataset sizes grow, optimizing model training performance becomes crucial to reduce training times and improve productivity. 

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This blog post delves into the history behind Trino introducing Alluxio as a replacement for RubiX as a file system cache. It explores the synergy between Trino and Alluxio, assesses which type of cache best suits various needs, and shares real-world examples of Trino and Alluxio adoption.

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Co-hosted by Alluxio and Uber on May 23, 2024, AI/ML Infra Meetup was the community event for developers focused on building AI, ML and data infrastructure at scale. We were thrilled by the overwhelming interest and enthusiasm in our meetup!

Performance, cache operability, and cost efficiency are key considerations for AI platform teams supporting large scale model training and distribution. In 2023, we launched Alluxio Enterprise AI, for managing AI training and model distribution I/O across diverse environments, whether in a single storage with diverse computing clusters or in a more complex multi-cloud, multi-data center environment.