In this talk, Bin Fan, VP of Technology at Alluxio, explores how to enable efficient data access across distributed GPU infrastructure, achieving low-latency performance for feature stores and RAG workloads.
In this talk, Bin Fan, VP of Technology at Alluxio, explores how to enable efficient data access across distributed GPU infrastructure, achieving low-latency performance for feature stores and RAG workloads.
Videos:
Presentation Slides:
In this talk, Bin Fan, VP of Technology at Alluxio, explores how to enable efficient data access across distributed GPU infrastructure, achieving low-latency performance for feature stores and RAG workloads.
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Videos
Hear from Zongheng Yang, Co-Creator of SkyPilot, as he explores how to simplify AI deployment across clouds and on-premises infrastructure with automated resource provisioning and cost optimization.
Amazon S3 and other cloud object stores have become the de facto storage system for organizations large and small. And it’s no wonder why. Cloud object stores deliver unprecedented flexibility with unlimited capacity that scales on demand and ensures data durability out-of-the-box at unbeatable prices.
Yet as workloads shift toward real-time AI, inference, feature stores, and agentic memory systems, S3’s latency and limited semantics begin to show their limits. In this webinar, you’ll learn how to augment — rather than replace — S3 with a tiered architecture that restores sub-millisecond performance, richer semantics, and high throughput — all while preserving S3’s advantages of low-cost capacity, durability, and operational simplicity.
We’ll walk through:
- The key challenges posed by latency-sensitive, semantically rich workloads (e.g. feature stores, RAG pipelines, write-ahead logs)
- Why “just upgrading storage” isn’t sufficient — the bottlenecks in metadata, object access latency, and write semantics
- How Alluxio transparently layers on top of S3 to provide ultra-low latency caching, append semantics, and zero data migration with both FSx-style POSIX access and S3 API access
- Real-world results: achieving sub-ms TTFB, 90%+ GPU utilization in ML training, 80X faster feature store query response times, and dramatic cost savings from reduced S3 operations
- Trade-offs, deployment patterns, and best practices for integrating this tiered approach in your AI/analytics stack
In this session, Pratik Mishra delivers insights on architecting scalable, deployable, and resilient AI infrastructure at scale. His discussion on fault tolerance, checkpoint optimization, and the democratization of AI compute through AMD's open ecosystem resonated strongly with the challenges teams face in production ML deployments.