Your 🐰 queries are slow 🐢 … you’re frustrated 😩 … Don’t let suboptimal Trino performance hold you back any longer! Unlock the full potential of Trino and transform your data analytics game. Discover the secrets behind Trino’s query engine and learn how to overcome bottlenecks to achieve⚡ blazing-fast query performance. In this comprehensive guide, … Continued
Tags: analytics, cloud, data, data orchestration, performance
In November’s Product School, Adit Madan, Director of Product Management at Alluxio, will highlights new features, enhanced manageability, improved security and performance in Alluxio 2.9 release.
In November’s Product School, Adit Madan, Director of Product Management at Alluxio, will highlights new features, enhanced manageability, improved security and performance in Alluxio 2.9 release.
Tags: data orchestration, performance, product release, product school
This talk covers how Uber’s Presto team implements the cache invalidation and dashboard for Alluxio’s Local Cache. Liang Chen will also share his experience using a customized cache filter to resolve the performance degradation due to a large working set.
Tags: alluxio day, local cache, performance, presto, uber
To provide model training with the best experience, Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to speed up Ceph storage for game AI training. This blog will give you insight into how Alluxio has been implemented and optimized at Tencent.
Tencent is one of the largest technology companies in the world and a leader in the gaming sector. The game AI platform supports AI research and development at Tencent. To provide model training with the best experience, Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to accelerate the game AI training.
Tags: ai, benchmark, case study, data analytics, MODEL TRAINING, performance, storage, tencent
This blog is the last one in the machine learning series. Our first blog introduced the what and why of our solution, and the second blog compared traditional and Alluxio solutions. This blog will demonstrate how to set up and benchmark the end-to-end performance of the training process.
This blog is the second in the machine learning series following the previous one, which discussed Alluxio’s solution to improve training performance and simplify data management. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process, thus improving training performance and simplifying data management. In this blog 2 of the series, we focus on comparing traditional solutions with Alluxio’s.
Running inference at scale is challenging. In this blog, we will share our observations and the practice to use Alluxio to speed up the I/O performance for large-scale ML/DL offline inference at Microsoft Bing.