As more and more companies turn to AI / ML / DL to unlock insight, AI has become this mythical word that adds unnecessary barriers to new adaptors. Oftentimes it was regarded as luxury for those big tech companies only – this should not be the case.
In this talk, Jingwen will first dissect the ML life cycle into five stages – starting from data collection, to data cleansing, model training, model validation, and end at model inference / deployment stages. For each stage, Jingwen will then go over its concept, functionality, characteristics, and use cases to demystify ML operations. Finally, Jingwen will showcase how Alluxio, a virtual data lake, could help simplify each stage.
As more and more companies turn to AI / ML / DL to unlock insight, AI has become this mythical word that adds unnecessary barriers to new adaptors. Oftentimes it was regarded as luxury for those big tech companies only – this should not be the case.
In this talk, Jingwen will first dissect the ML life cycle into five stages – starting from data collection, to data cleansing, model training, model validation, and end at model inference / deployment stages. For each stage, Jingwen will then go over its concept, functionality, characteristics, and use cases to demystify ML operations. Finally, Jingwen will showcase how Alluxio, a virtual data lake, could help simplify each stage.
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
