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Coupang, a Fortune 200 technology company, manages a multi-cluster GPU architecture for their AI/ML model training. This architecture introduced significant challenges, including:

1. Time-consuming data preparation and data copy/movement
2. Difficulty utilizing GPU resources efficiently
3. High and growing storage costs
4. Excessive operational overhead maintaining storage for localized data silos

To resolve these challenges, Coupang’s AI platform team implemented a distributed caching system that automatically retrieves training data from their central data lake, improves data loading performance, unifies access paths for model developers, automates data lifecycle management, and extends easily across Kubernetes environments. The new distributed caching architecture has improved model training speed, reduced storage costs, increased GPU utilization across clusters, lowered operational overhead, enabled training workload portability, and delivered 40% better I/O performance compared to parallel file systems.

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.

Insights from from Uber, Snap, and Alluxio on LLM training, fine-tuning, deployment, designing scalable architectures, GPU optimization, and building recommendations systems.