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In this tech talk, Hyun Jung Baek, Staff Backend Engineer at Coupang, will share best practices for leveraging distributed caching to power search and recommendation model training infrastructure.

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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Co-hosted by Alluxio and the Uber AI team on March 6, 2025, at Uber's Seattle office and via Zoom, the AI/ML Infra Meetup is a community event for developers focused on building AI, ML, and data infrastructure at scale. Speakers from Uber, Snap, and Alluxio delivered talks, sharing insights and real-world examples about LLM training, fine-tuning, deployment, designing scalable architectures, GPU optimization, and building recommendations systems.

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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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