Tag: hybrid cloud

How Can AI Platforms Adapt to Hybrid or Multi-Cloud Environments?

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This article was originally published on Spiceworks. https://www.spiceworks.com/tech/artificial-intelligence/guest-article/adapting-ai-platform-to-hybrid-cloud/ This blog discusses the challenges of implementing AI platforms in hybrid and multi-cloud environments and shares examples of organizations that have prioritized security and optimized cost management using the data access layer. In recent years, AI platforms have undergone significant transformations as GenAI and AI continue to … Continued

Achieving Hybrid and Multi-Cloud Architecture With Application Portability

by Fortune 50 Technology Company

A Fortune 50 technology company that serves over 1 billion users successfully implemented Alluxio to achieve a hybrid cloud strategy, become multi-cloud ready, cut costs, and boost agility. This case study highlights: This tech giant’s cloud journey to modernize its data platform and the challenges it faces Why this company chose Alluxio to achieve hybrid … Continued

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Alluxio Use Cases Overview

Alluxio started as a virtual distributed file system, a research project out of the AMPLab at U.C. Berkeley. Alluxio foresaw the need for agility when accessing large data stores separated from compute engines like Hadoop or Spark.
Fast forward several years and over a thousand committers later, and Alluxio has blossomed into the industry’s leading data orchestration platform for analytics and AI/ML. But as with any new type of technology, figuring out the best ways to use it depends on your data environment, computational workloads, issues, and goals. 

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Introducing what’s new in Alluxio 2.5

Alluxio 2.5 focuses on improving interface support to broaden the set of data driven applications which can benefit from data orchestration. The POSIX and S3 client interfaces have greatly improved in performance and functionality as a result of the widespread usage and demand from AI/ML workloads and system administration needs. Alluxio is rapidly evolving to meet the needs of enterprises that are deploying it as a key component of their AI/ML stacks.

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