Blog
Suresh Kumar Veerapathiran and Anudeep Kumar, engineering leaders at Uptycs, recently shared their experience of evolving their data platform and analytics architecture to power analytics through a generative AI interface. In their post on Medium titled Cache Me If You Can: Building a Lightning-Fast Analytics Cache at Terabyte Scale, Veerapathiran and Kumar provide detailed insights into the challenges they faced (and how they solved them) scaling their analytics solution that collects and reports on terabytes of telemetry data per day as part of Uptycs Cloud-Native Application Protection Platform (CNAPP) solutions.
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With the new year comes new features in Alluxio Enterprise AI! Just weeks into 2025 and we are already bringing you exciting new features to better manage, scale, and secure your AI data with Alluxio. From advanced cache management and improved write performance to our Python SDK and S3 API enhancements, our latest release of Alluxio Enterprise AI delivers more power and performance to your AI workloads. Without further ado, let’s dig into the details.
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Bringing a large language model from its initial training to deployment requires numerous systems and components. At Zhihu, we grappled with a multi-cloud, cross-region AI platform, requiring an efficient solution to facilitate the rapid training and delivery of models for production use cases. This led us to adopt Alluxio, the high-performance data access layer for LLM. This blog provides an in-depth look at Zhihu’s challenges, journey, and solution for LLM training and deployment. Through adopting Alluxio, we’ve significantly enhanced model training performance by 2 to 3 times and can deploy updated models every minute instead of hours or days. Also, our GPU utilization has doubled, infrastructure and operation costs have been halved, and we have established a resilient, efficient infrastructure capable of meeting our escalating AI demands.
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This is part 2 of the blog series talking about the design and implementation of the Cross Cluster Synchronization mechanism in Alluxio. In the previous blog, we discussed the scenario, background and how metadata sync is done with a single Alluxio cluster. This blog will describe how metadata sync is built upon to provide metadata consistency in a multi-cluster scenario.