A collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problems of Deep Learning model training in the cloud. Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost.
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This article describes how Alluxio can accelerate the training of deep learning models in a hybrid cloud environment when using Intel’s Analytics Zoo open source platform, powered by oneAPI. Details on the new architecture and workflow, as well as Alluxio’s performance benefits and benchmarks results will be discussed.
Are you using SQL engines, such as Presto, to query existing Hive data warehouse and experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications?
The DBS team was tasked to solve their compute capacity problem. They wanted to provide faster insights and analyze data for a range of … Continued
In this panel, creators of open source projects share their stories from why they started the project to the challenges they encountered on the … Continued
This session talks about challenges associated with querying diverse data sources at Walmart and how those are tackled using Presto & Alluxio. … Continued
In this talk, we share our lessons in building and rebuilding our monitoring systems and data platforms at Electronic Arts (EA). … Continued
Best use cases for Presto from the Data Engineer’s perspective. Also hear about recent Presto advancements such as Cost-Based Optimizer, Kubernetes-native deployment and the … Continued
The whole Ryte-Platform is built with a scalable architecture to support our heavy load and make it possible for our customers to drill-down from … Continued